{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-layer FNN on Cifar\n",
    "\n",
    "This is MLP (784-X^W-10) on MNIST. SGD algorithm (lr=0.1) with 100 epoches.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import locale\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract final stats from resman's diary file\"\"\"\n",
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time\n",
    "\n",
    "\"\"\" Extract number of total parameters for each net config from resman's diary file\"\"\"\n",
    "def parse_num_params(line0):\n",
    "    line_str = ''.join(lines0)\n",
    "    idx = line_str.find(\"Total params\")\n",
    "    param_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(locale.atof(param_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract results from diary file\n",
    "\n",
    "    1. Number of params\n",
    "    2. Loss/Accuarcy for training/testing\n",
    "    3. Runing time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1000, 2000, 3000, 4000, 5000, 5250, 5500, 5750, 6000, 6250, 6500, 6750, 7000, 7250, 7500, 7750, 8000, 8500, 9000, 10000, 12500, 15000]\n"
     ]
    }
   ],
   "source": [
    "results_dir = '../results/fnn_cifar_l2_run2'\n",
    "       \n",
    "depth = [1,2,3,4,5]\n",
    "width = [50,100,200,400]\n",
    "# dim   = [0,600,800,1000,1250,1500]\n",
    "\n",
    "sr=0.9 # solved ratio\n",
    "\n",
    "########## 1. filename list of diary ########################\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "\n",
    "# print diary_names            \n",
    "dim_ = []            \n",
    "for f in diary_names: \n",
    "    dim_.append( int(f.split('/')[-2].split('_')[-3]  ) )         \n",
    "\n",
    "dim = list(set(dim)) \n",
    "dim = sorted(dim) \n",
    " \n",
    "print dim\n",
    "             \n",
    "             \n",
    "\n",
    "\n",
    "########## 2. Construct stats (width, depth, dim) ##########\n",
    "# acc_test_all : Tensor (width, depth, dim)\n",
    "# num_param_all: Tensor (width, depth)\n",
    "# acc_solved_all:  Tensor (width, depth)\n",
    "# dim_solved_all:  Tensor (width, depth)\n",
    "############################################################\n",
    "nw, nd, nn= len(width), len(depth), len(dim)\n",
    "\n",
    "acc_test_all  = np.zeros((len(width), len(depth), len(dim)))\n",
    "num_param_all = np.zeros((len(width), len(depth)))\n",
    "acc_solved_all = np.zeros((len(width), len(depth)))\n",
    "dim_solved_all = np.ones((len(width), len(depth)))*dim[-1]\n",
    "\n",
    "mode = 1         # {0: test loss, 1: test acc}\n",
    "error_files = [] #  record the error file\n",
    "\n",
    "# 2.1 construct acc_test_all and num_param_all\n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            # 2.1.1 Read the results, \n",
    "            for f in diary_names:\n",
    "                if '_'+str(d)+'_'+str(ll)+'_'+str(w)+'/' in f:\n",
    "                    # print \"%d is in\" % d + f\n",
    "                    \n",
    "                    with open(f,'r') as ff:\n",
    "                        lines0 = ff.readlines()\n",
    "                        try:\n",
    "                            R = extract_num(lines0)\n",
    "                            R = np.array(R)\n",
    "\n",
    "                        except ValueError:\n",
    "                            error_files.append((w,ll,d))\n",
    "                            R = np.zeros(len(R))\n",
    "                            print \"Error. Can not read config: depth %d, width %d and dim %d.\" % (ll, w, d) \n",
    "                            # break\n",
    "                            \n",
    "                            \n",
    "            # 2.1.2 Assign the results\n",
    "            r = R[mode]  \n",
    "            acc_test_all[id_w,id_ll,id_d]=r\n",
    "            if d==0:\n",
    "                num_param_all[id_w,id_ll]=parse_num_params(lines0) \n",
    "                \n",
    "# 2.2 construct acc_solved_all and dim_solved_all           \n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            r = acc_test_all[id_w,id_ll,id_d]\n",
    "            if d==0:\n",
    "                test_acc_bl = r  # 0.5 #   \n",
    "                # print \"Acc goal is: \" + str(test_acc_sl) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "            else:\n",
    "                test_acc = r\n",
    "                if test_acc>test_acc_bl*sr:\n",
    "                    acc_solved_all[id_w,id_ll]=test_acc\n",
    "                    dim_solved_all[id_w,id_ll]=d\n",
    "                    # print \"Intrinsic dim is: \" + str(d) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "                    # print \"\\n\"\n",
    "                    break\n",
    "                    \n",
    "\n",
    "########## 3. Construct Tensors for Analysis (width, depth, dim) ##########                    \n",
    "acc_base  = acc_test_all[:,:,0]\n",
    "acc_solve = acc_base*sr\n",
    "                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline results\n",
      "(4, 5, 23)\n",
      "# Parmas\n",
      "[[  154160.   156710.   159260.   161810.   164360.]\n",
      " [  308310.   318410.   328510.   338610.   348710.]\n",
      " [  616610.   656810.   697010.   737210.   777410.]\n",
      " [ 1233210.  1393610.  1554010.  1714410.  1874810.]]\n",
      "Cross-line results\n",
      "[[ 0.4288  0.417   0.4156  0.4194  0.4357]\n",
      " [ 0.4185  0.4248  0.4299  0.4294  0.4258]\n",
      " [ 0.4475  0.4421  0.4527  0.4539  0.4504]\n",
      " [ 0.4595  0.4587  0.4582  0.4682  0.    ]]\n",
      "Cross-line Dim\n",
      "[[  4000.   3000.   3000.   3000.   4000.]\n",
      " [  3000.   4000.   4000.   4000.   4000.]\n",
      " [  5250.   5000.   6250.   7250.   6500.]\n",
      " [  6250.   7500.   9000.  12500.  15000.]]\n",
      "Dim 15000 results\n",
      "[[ 0.4797  0.4757  0.4654  0.4669  0.4691]\n",
      " [ 0.4815  0.4795  0.4679  0.4613  0.4558]\n",
      " [ 0.4865  0.474   0.4636  0.4693  0.4624]\n",
      " [ 0.4836  0.468   0.4691  0.4633  0.4566]]\n"
     ]
    }
   ],
   "source": [
    "print \"Baseline results\"\n",
    "print acc_test_all.shape\n",
    "\n",
    "print \"# Parmas\"\n",
    "print num_param_all\n",
    "\n",
    "print \"Cross-line results\"\n",
    "print acc_solved_all\n",
    "\n",
    "print \"Cross-line Dim\"\n",
    "print dim_solved_all\n",
    "\n",
    "print \"Dim %d results\" % dim[-1]\n",
    "print acc_test_all[:,:,-1]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List the config of depth and width, which yields errors in training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in the following configs: width, depth and dim\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "Shape of accuracy tensor: (4, 5, 460)\n"
     ]
    }
   ],
   "source": [
    "E_width, E_depth, E_dim = [],[],[]\n",
    "for item in error_files:\n",
    "    E_width.append(item[0]) \n",
    "    E_depth.append(item[1])\n",
    "    E_dim.append(item[2])\n",
    "\n",
    "str_E_width = \"\".join(str(E_width)).replace(',', '')\n",
    "str_E_depth = \"\".join(str(E_depth)).replace(',', '')\n",
    "str_E_dim   = \"\".join(str(E_dim)).replace(',', '')\n",
    "\n",
    "print \"Error in the following configs: width, depth and dim\"\n",
    "print str_E_width\n",
    "print str_E_depth\n",
    "print str_E_dim\n",
    "\n",
    "print \"Shape of accuracy tensor: \" + str(acc_test_all.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the accuracy of specific depth and width, along different dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_cfg_results (depth, width, lines0): \n",
    "    for d in dim:\n",
    "        # 1. read the results\n",
    "        for f in diary_names:\n",
    "            if '_'+str(d)+'_'+str(depth)+'_'+str(width)+'/' in f:\n",
    "                # print \"%d is in\" % d + f\n",
    "                diary_names_ordered.append(f)\n",
    "                with open(f,'r') as ff:\n",
    "                    lines0 = ff.readlines()\n",
    "                    try:\n",
    "                        # print lines0\n",
    "                        param_num = parse_num_params(lines0)\n",
    "                        R = extract_num(lines0)\n",
    "                        print R[1]\n",
    "\n",
    "                    except ValueError:\n",
    "                        print \"Error: Can not read\"\n",
    "                        break                                               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape the tensor to 1D for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50, 100, 200, 400, 50, 100, 200, 400, 50, 100, 200, 400, 50, 100, 200, 400, 50, 100, 200, 400]\n",
      "[1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5]\n",
      "[[  154160.   156710.   159260.   161810.   164360.]\n",
      " [  308310.   318410.   328510.   338610.   348710.]\n",
      " [  616610.   656810.   697010.   737210.   777410.]\n",
      " [ 1233210.  1393610.  1554010.  1714410.  1874810.]]\n",
      "Ratio of D: 12.1614556305\n",
      "[[  4000.   3000.   3000.   3000.   4000.]\n",
      " [  3000.   4000.   4000.   4000.   4000.]\n",
      " [  5250.   5000.   6250.   7250.   6500.]\n",
      " [  6250.   7500.   9000.  12500.  15000.]]\n",
      "Ratio of D: 5.0\n",
      "[  154160.   308310.   616610.  1233210.   156710.   318410.   656810.\n",
      "  1393610.   159260.   328510.   697010.  1554010.   161810.   338610.\n",
      "   737210.  1714410.   164360.   348710.   777410.  1874810.]\n",
      "[  4000.   3000.   5250.   6250.   3000.   4000.   5000.   7500.   3000.\n",
      "   4000.   6250.   9000.   3000.   4000.   7250.  12500.   4000.   4000.\n",
      "   6500.  15000.]\n",
      "[1000, 0.3453],[2000, 0.3857],[3000, 0.4142],[4000, 0.4288],[5000, 0.4447],[5250, 0.4338],[5500, 0.4506],[5750, 0.4464],[6000, 0.4545],[6250, 0.451],[6500, 0.4563],[6750, 0.4535],[7000, 0.4533],[7250, 0.4587],[7500, 0.467],[7750, 0.4673],[8000, 0.4641],[8500, 0.4611],[9000, 0.4652],[10000, 0.4638],[12500, 0.4787],[15000, 0.4797],[1000, 0.3577],[2000, 0.3884],[3000, 0.417],[4000, 0.4265],[5000, 0.4379],[5250, 0.4435],[5500, 0.4486],[5750, 0.4445],[6000, 0.4486],[6250, 0.445],[6500, 0.4512],[6750, 0.4521],[7000, 0.4538],[7250, 0.4539],[7500, 0.4554],[7750, 0.4551],[8000, 0.4574],[8500, 0.4594],[9000, 0.4633],[10000, 0.4652],[12500, 0.4682],[15000, 0.4757],[1000, 0.3438],[2000, 0.4016],[3000, 0.4156],[4000, 0.423],[5000, 0.4396],[5250, 0.4413],[5500, 0.45],[5750, 0.4433],[6000, 0.4419],[6250, 0.4451],[6500, 0.4437],[6750, 0.445],[7000, 0.4531],[7250, 0.4487],[7500, 0.4547],[7750, 0.4482],[8000, 0.461],[8500, 0.4589],[9000, 0.4511],[10000, 0.4567],[12500, 0.468],[15000, 0.4654],[1000, 0.3574],[2000, 0.3948],[3000, 0.4194],[4000, 0.4324],[5000, 0.4323],[5250, 0.4362],[5500, 0.4409],[5750, 0.4472],[6000, 0.4415],[6250, 0.4473],[6500, 0.4565],[6750, 0.4496],[7000, 0.4518],[7250, 0.4521],[7500, 0.4533],[7750, 0.4473],[8000, 0.4537],[8500, 0.4447],[9000, 0.4541],[10000, 0.4664],[12500, 0.4589],[15000, 0.4669],[1000, 0.362],[2000, 0.3944],[3000, 0.4127],[4000, 0.4357],[5000, 0.429],[5250, 0.4436],[5500, 0.4419],[5750, 0.4376],[6000, 0.4374],[6250, 0.4437],[6500, 0.4458],[6750, 0.4415],[7000, 0.4511],[7250, 0.4578],[7500, 0.4434],[7750, 0.446],[8000, 0.4603],[8500, 0.4559],[9000, 0.45],[10000, 0.4623],[12500, 0.4619],[15000, 0.4691],[1000, 0.3476],[2000, 0.3907],[3000, 0.4185],[4000, 0.4352],[5000, 0.4434],[5250, 0.4519],[5500, 0.4498],[5750, 0.4461],[6000, 0.4537],[6250, 0.4619],[6500, 0.4584],[6750, 0.4524],[7000, 0.4657],[7250, 0.463],[7500, 0.466],[7750, 0.4599],[8000, 0.4635],[8500, 0.4695],[9000, 0.4702],[10000, 0.4797],[12500, 0.4784],[15000, 0.4815],[1000, 0.3527],[2000, 0.3965],[3000, 0.4146],[4000, 0.4248],[5000, 0.4473],[5250, 0.449],[5500, 0.4531],[5750, 0.4436],[6000, 0.4556],[6250, 0.4524],[6500, 0.4562],[6750, 0.4562],[7000, 0.4505],[7250, 0.4582],[7500, 0.4615],[7750, 0.4537],[8000, 0.458],[8500, 0.4589],[9000, 0.4568],[10000, 0.4707],[12500, 0.468],[15000, 0.4795],[1000, 0.353],[2000, 0.398],[3000, 0.4202],[4000, 0.4299],[5000, 0.4375],[5250, 0.4411],[5500, 0.4455],[5750, 0.4412],[6000, 0.4505],[6250, 0.4496],[6500, 0.443],[6750, 0.4544],[7000, 0.4532],[7250, 0.453],[7500, 0.4592],[7750, 0.45],[8000, 0.463],[8500, 0.4644],[9000, 0.465],[10000, 0.4667],[12500, 0.47],[15000, 0.4679],[1000, 0.3548],[2000, 0.3957],[3000, 0.4222],[4000, 0.4294],[5000, 0.4413],[5250, 0.4469],[5500, 0.4393],[5750, 0.4451],[6000, 0.4453],[6250, 0.4517],[6500, 0.4503],[6750, 0.4544],[7000, 0.4484],[7250, 0.454],[7500, 0.4504],[7750, 0.4511],[8000, 0.4541],[8500, 0.4489],[9000, 0.4608],[10000, 0.453],[12500, 0.4632],[15000, 0.4613],[1000, 0.3504],[2000, 0.398],[3000, 0.4115],[4000, 0.4258],[5000, 0.4315],[5250, 0.4409],[5500, 0.4422],[5750, 0.4479],[6000, 0.4412],[6250, 0.4425],[6500, 0.4468],[6750, 0.4439],[7000, 0.4551],[7250, 0.4532],[7500, 0.4554],[7750, 0.452],[8000, 0.4496],[8500, 0.4518],[9000, 0.4532],[10000, 0.4576],[12500, 0.4629],[15000, 0.4558],[1000, 0.3525],[2000, 0.3955],[3000, 0.4152],[4000, 0.4344],[5000, 0.4422],[5250, 0.4475],[5500, 0.4468],[5750, 0.453],[6000, 0.4503],[6250, 0.4509],[6500, 0.4586],[6750, 0.4552],[7000, 0.463],[7250, 0.4561],[7500, 0.4663],[7750, 0.4675],[8000, 0.4569],[8500, 0.4651],[9000, 0.4639],[10000, 0.4676],[12500, 0.4752],[15000, 0.4865],[1000, 0.3546],[2000, 0.3997],[3000, 0.4176],[4000, 0.434],[5000, 0.4421],[5250, 0.4434],[5500, 0.4521],[5750, 0.4485],[6000, 0.4468],[6250, 0.4471],[6500, 0.4478],[6750, 0.4516],[7000, 0.459],[7250, 0.4491],[7500, 0.4607],[7750, 0.4637],[8000, 0.4552],[8500, 0.4559],[9000, 0.4684],[10000, 0.4615],[12500, 0.4719],[15000, 0.474],[1000, 0.3539],[2000, 0.3947],[3000, 0.4119],[4000, 0.4266],[5000, 0.4383],[5250, 0.4358],[5500, 0.4358],[5750, 0.4422],[6000, 0.4454],[6250, 0.4527],[6500, 0.4534],[6750, 0.4561],[7000, 0.448],[7250, 0.4548],[7500, 0.4533],[7750, 0.4496],[8000, 0.4488],[8500, 0.4612],[9000, 0.4605],[10000, 0.4641],[12500, 0.4709],[15000, 0.4636],[1000, 0.3626],[2000, 0.3958],[3000, 0.4144],[4000, 0.4273],[5000, 0.434],[5250, 0.4382],[5500, 0.4385],[5750, 0.444],[6000, 0.4373],[6250, 0.4463],[6500, 0.4519],[6750, 0.4487],[7000, 0.4413],[7250, 0.4539],[7500, 0.4494],[7750, 0.4519],[8000, 0.4574],[8500, 0.4508],[9000, 0.4588],[10000, 0.4549],[12500, 0.4591],[15000, 0.4693],[1000, 0.3584],[2000, 0.3914],[3000, 0.4143],[4000, 0.4226],[5000, 0.4316],[5250, 0.4419],[5500, 0.4355],[5750, 0.4342],[6000, 0.4367],[6250, 0.4439],[6500, 0.4504],[6750, 0.4515],[7000, 0.4527],[7250, 0.4499],[7500, 0.4557],[7750, 0.45],[8000, 0.4423],[8500, 0.449],[9000, 0.4617],[10000, 0.4625],[12500, 0.4618],[15000, 0.4624],[1000, 0.3549],[2000, 0.3945],[3000, 0.4114],[4000, 0.4365],[5000, 0.4442],[5250, 0.4425],[5500, 0.4447],[5750, 0.4446],[6000, 0.4487],[6250, 0.4595],[6500, 0.4556],[6750, 0.4632],[7000, 0.457],[7250, 0.4571],[7500, 0.4632],[7750, 0.4591],[8000, 0.4674],[8500, 0.4643],[9000, 0.4644],[10000, 0.4714],[12500, 0.4779],[15000, 0.4836],[1000, 0.3582],[2000, 0.3984],[3000, 0.408],[4000, 0.4284],[5000, 0.4351],[5250, 0.446],[5500, 0.4407],[5750, 0.4482],[6000, 0.4478],[6250, 0.4533],[6500, 0.4565],[6750, 0.4516],[7000, 0.4505],[7250, 0.4542],[7500, 0.4587],[7750, 0.4593],[8000, 0.4596],[8500, 0.457],[9000, 0.4632],[10000, 0.4639],[12500, 0.4719],[15000, 0.468],[1000, 0.3587],[2000, 0.3949],[3000, 0.4107],[4000, 0.4352],[5000, 0.4357],[5250, 0.443],[5500, 0.4386],[5750, 0.4423],[6000, 0.4421],[6250, 0.4469],[6500, 0.4517],[6750, 0.448],[7000, 0.4512],[7250, 0.452],[7500, 0.4525],[7750, 0.4507],[8000, 0.4501],[8500, 0.4553],[9000, 0.4582],[10000, 0.4583],[12500, 0.4652],[15000, 0.4691],[1000, 0.3532],[2000, 0.3861],[3000, 0.4069],[4000, 0.428],[5000, 0.44],[5250, 0.4375],[5500, 0.4423],[5750, 0.4365],[6000, 0.4479],[6250, 0.4438],[6500, 0.4435],[6750, 0.4442],[7000, 0.4439],[7250, 0.4503],[7500, 0.4468],[7750, 0.4538],[8000, 0.4508],[8500, 0.4493],[9000, 0.4486],[10000, 0.4525],[12500, 0.4682],[15000, 0.4633],[1000, 0.3617],[2000, 0.3894],[3000, 0.4126],[4000, 0.4241],[5000, 0.4263],[5250, 0.4375],[5500, 0.4409],[5750, 0.4386],[6000, 0.4423],[6250, 0.4381],[6500, 0.4385],[6750, 0.4411],[7000, 0.4422],[7250, 0.445],[7500, 0.4443],[7750, 0.4448],[8000, 0.4521],[8500, 0.4501],[9000, 0.4533],[10000, 0.4522],[12500, 0.4599],[15000, 0.4566]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig_width = width*len(depth)\n",
    "fig_depth = list(itertools.chain.from_iterable(itertools.repeat(x, len(width)) for x in depth))\n",
    "\n",
    "print fig_width\n",
    "print fig_depth\n",
    "print num_param_all\n",
    "print \"Ratio of D: \" + str(np.max(num_param_all[:])/(np.min(num_param_all[:])+.0))\n",
    "print dim_solved_all\n",
    "print \"Ratio of D: \" + str(np.max(dim_solved_all[:])/(np.min(dim_solved_all[:])+.0))\n",
    "fig_params_1d = num_param_all.reshape(len(depth)*len(width),order='F')\n",
    "dim_solved_all_1d = dim_solved_all.reshape(len(depth)*len(width),order='F')\n",
    "acc_solved_all_1d = acc_solved_all.reshape(len(depth)*len(width),order='F')\n",
    "print fig_params_1d\n",
    "print dim_solved_all_1d\n",
    "\n",
    "\n",
    "\n",
    "dim3 = np.repeat(dim, 4*5, axis=0).reshape([4, 5, len(dim)],order='F')\n",
    "acc_test_all_d1 =  acc_test_all[:,:,1:].reshape((len(dim)-1)*4*5)\n",
    "dim_d1 =  dim3[:,:,1:].reshape((len(dim)-1)*4*5)\n",
    "subspace =  np.array([ (int(dim_d1[i]), acc_test_all_d1[i]) for i in range(len(dim_d1)) ])\n",
    "\n",
    "\n",
    "print ','.join(['[%i, %s]' % (subspace[n,0], subspace[n,1]) for n in xrange(len(subspace))])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Accuracy wrt. Width, Depth and Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e570ab450>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R3aH93hFHHLFj5cqVT+19vKioqHb+/PnP90RMTZQoEhEREREREYkbI6yVzKQXU6JIRERE\nREREJE40okh6O92dIiIiIiIiIiICaESRiIiIiIiISFxp6pn0ZkoUiYiIiIiIiMSJu2nqmfRqShSJ\niIiIiIiIxFFYiSLpxZQoEhEREREREYkTByKaeia9mBJFIiIiIiIiInFjGlEkvZoSRSIiIiIiIiJx\n4kDENaJIei8likRERERERETiKIxGFEnvpUSRiIiIiIiISJw4phFF0qvFNI1pZqeb2SIzW2pmt7Vw\nfoSZvWpm75vZR2Z2ZizjEREREREREelpEQId2kTiKWYjiswsAXgAOAVYA7xjZnPcfWGzZt8G/uju\nvzCzccBcoDhWMYmIiIiIiIj0JHcIa0SR9GKxTE0eCSx192XuXgc8AXxhrzYOZDT+nAmsi2E8IiIi\nIiIiIj0u4tahTfqfU0455XMZGRlfKiwsvLDp2OrVq5MnTpx4VkFBwYyJEyeetWbNmiSASCTCueee\ne2x+fv6MESNGXDB37tycWMYWyxpFw4DVzfbXAEft1eY7wEtmdiOQDpzcUkdmdh1wHUB+fj6lpaXd\nHauI7Geqqqr0t0RERETapc8M0t0aahRpOtn+7qqrrlo0c+bMBddff/3UpmO33nrrpKOPPnrtL3/5\nyw+uv/76SbfeeuuhTzzxxFuzZ88uXLVqVeb69euf+NOf/pR30003HX/mmWf+KVax9XQx64uB37j7\nj83sGOD3ZjbB3SPNG7n7bGA2wOTJk33KlCnxj1RE+pXS0lL0t0RERETao88MEgthNEpof3fxxRdv\neO+99wY0P/baa68Vv/rqq88BzJw5c/HUqVPPBt6aM2dO8YUXXrg4EAgwffr0jV/96leTy8rK0kpK\nSqpjEVss05hrgcJm+8MbjzV3NfBHAHf/J5ACxHQIlYiIiIiIiEhPcTT1TFpWWVmZ2pT8GTt2bHVl\nZWUqwMaNG9MPOOCAnU3tcnJydi5ZsiQtVnHEMlH0DjDGzEaaWRIwA5izV5tVwOcBzOwgGhJFm2IY\nk4iIiIiIiEgPaph61pFNet7ZZ599XEJCwrVnn332cfG4XiAQwKxnkoQxu+PcPQR8DfgL8AkNq5st\nMLO7zOycxmb/DlxrZh8CjwNXuLvHKiYRERERERGRnhbBOrRFy8wSzOx9M3u+hXPJZvYHM1tqZm+Z\nWXE3vqV+b+7cueMikYjNnTt3XKyukZmZWVNWVpYGUFZWlpaRkVEDkJeXt3PZsmXpTe0qKirSx4wZ\nE5NpZxDbEUW4+1x3H+vuo9z9e43H7nD3OY0/L3T349x9ortPcveXYhmPiIiIiIiISD/2dRoGarTk\namCru48GZgH3xC2qfuDMM89cGAgE/Mwzz1wYq2uccMIJK2bNmjUWYNasWWNPPPHEFQBnn332yief\nfHJsJBLhmWeeyUtPT6+LVX0i6Pli1iIiIiIiIiL7DXcIx6DukJkNB84Cvgfc0kKTL9Cw8jjAU8DP\nzMw0qyc6zz333D+Af3RXfyeeeOLnP/rooyFVVVUpWVlZl371q1+dd++9934wbdq0UwoKCkry8vJ2\nvPDCC68AXH/99avmzp1bOGTIkBlJSUmhn//856XdFUdLlCgSERERERERiaNO1B3KMbN5zfZnN64O\n3tx9wDeAga30MQxYDQ2lYsysEsgGKjoajHTda6+99teWjs+fP3+faYOBQKApURUXShSJiIiIiIiI\nxInTqZXMKtx9cmsnzWwasNHd3zWzKV2JT0Tl00VERERERETiKAbFrI8DzjGzFcATwElm9uhebdYC\nhQBmFgQygc3d966kv1CiSERERERERCROHIi4dWhrt0/32919uLsXAzOAv7n7ZXs1mwNc3vjzBY1t\nVJ9I9qGpZyIiIiIiIiJx1IkaRZ1iZncB8xpXHv818HszWwpsoSGhJLIPJYpERERERERE4iXKUUKd\n7t69FCht/PmOZsd3ARfG7MLSbyhRJCIiIiIiIhInDtHWHRLpEUoUiYiIiIiIiMRRLEcUiXSVEkUi\nIiIiIiIicdJUzFqkt1KiSERERERERCSOlCiS3kyJIhEREREREZE4cWJbzFqkq5QoEhEREREREYkj\nFbOW3kyJIhEREREREZF4cU09k95NiSIRERERERGROFExa+ntAj0dgIiIiIiIiMj+JOLWoU36l/nz\n56dPmDBh2rBhw744fPjwC2+++eYJAKtXr06eOHHiWQUFBTMmTpx41po1a5IAIpEI55577rH5+fkz\nRowYccHcuXNzYhmfEkUiIiIiIiIiInGSmJjoP/zhD/+1du3aP77//vt/+sMf/jC+tLR00K233jrp\n6KOPXrthw4Ynjj766LW33nrroQCzZ88uXLVqVeb69eufuO+++1676aabjo9lfEoUiYiIiIiIiMRJ\n06pnGlG0/yopKak+88wzKwByc3PrCwsLty1btiz9tddeK545c+ZigJkzZy7++9//XgwwZ86c4gsv\nvHBxIBBg+vTpG3fu3JlcVlaWFqv4lCgSERERERERiSN369AmPau8vDzx2GOPPTkrK+vSY4899uTy\n8vLE7ur7vffeG/Dpp59mn3XWWRsrKytTS0pKqgHGjh1bXVlZmQqwcePG9AMOOGBn02tycnJ2Llmy\nRIkiERERERERkf4ggnVok5513nnnfe6dd94p2rp1a/o777xTNH369M91R78VFRXBCy644NRvf/vb\nb+bn59c3PxcIBDDrmf/2ShSJiIiIiIiIxIm7iln3NWVlZfmhUCgBIBQKJXzyySf5Xe2zpqYmcPLJ\nJ5961llnLZk5c+YKgMzMzJqmKWVlZWVpGRkZNQB5eXk7ly1blt702oqKivQxY8ZUdzWG1sQ0UWRm\np5vZIjNbama3tXB+lpl90LgtNrNtsYxHREREREREpKdp6lnfUlJSUh4MBsMAwWAwfNBBB5V3pb9I\nJMJZZ531uZEjR2796U9/Or/p+AknnLBi1qxZYwFmzZo19sQTT1wBcPbZZ6988sknx0YiEZ555pm8\n9PT0uqYparEQjFXHZpYAPACcAqwB3jGzOe6+sKmNu89s1v5G4NBYxSMiIiIiIiLS8zRKqK959tln\n/z59+vTPffLJJ/kHHXRQ+TPPPPP3rvT35JNPFrz66qtjCgsLtxQVFZ0PcPvtt7997733fjBt2rRT\nCgoKSvLy8na88MILrwBcf/31q+bOnVs4ZMiQGUlJSaGf//znpd3wtloVs0QRcCSw1N2XAZjZE8AX\ngIWttL8YuDOG8YiIiIiIiIj0OI0S6lvy8/Pr//GPf7zSXf1ddNFFGy666KLZLZ2bP3/+83sfCwQC\nPPfcc//oruu3J5aJomHA6mb7a4CjWmpoZkXASOBvrZy/DrgOID8/n9LS0m4NVET2P1VVVfpbIiIi\nIu3SZwbpbg4aUSS9WiwTRR0xA3jK3cMtnXT32cBsgMmTJ/uUKVPiGJqI9EelpaXob4mIiIi0R58Z\npNt5Q0Frkd4qlomitUBhs/3hjcdaMgP4agxjEREREREREekVtOS99GaxTBS9A4wxs5E0JIhmAJfs\n3cjMSoDBwD9jGIuIiIiIiIhIj3NUo0h6t5glitw9ZGZfA/4CJAAPu/sCM7sLmOfucxqbzgCecNfg\nOxEREREREenvtOqZ9G4xrVHk7nOBuXsdu2Ov/e/EMgYRERERERGR3kTDJKQ36y3FrEVERERERET2\nC5p6Jr2ZEkUiIiIiIiIiceKuRJH0boGeDkBERERERERkfxJx69DWHjNLMbO3zexDM1tgZt9toc0V\nZrbJzD5o3K6JyZuTPk8jikRERERERETiKAY1imqBk9y9yswSgTfM7M/u/q+92v3B3b/W7VeXfkWJ\nIhEREREREZE+rHEV8arG3cTGTSWzpVM09UxEREREREQkjtytQxuQY2bzmm3X7d2nmSWY2QfARuBl\nd3+rhUufb2YfmdlTZlYY47cpfZRGFImIiIiIiIjEiWOdKWZd4e6T2+zXPQxMMrNBwLNmNsHdP27W\n5DngcXevNbPrgd8CJ3U0EOn/NKJIREREREREJI68g1uH+nbfBrwKnL7X8c3uXtu4+xBweGfjl+5R\nX19vxcXF50+ePPl0gHfffXfg6NGjz83Pz59x/PHHf766ujoAUFVVFTj++OM/n5+fP2P06NHnvvfe\newNiGZcSRSIiIiIiIiLx4p2aetYmM8ttHEmEmaUCpwBle7UZ0mz3HOCTbnxX/Vp1dXXgJz/5ycgv\nf/nLE3/yk5+MbErgdNUtt9wyoaioaGvT/te//vWjrrnmmvnl5eVPZGZm1n3nO98pAfjud79bkpmZ\nWVdeXv7ENddcM/+mm246qjuu3xolikRERERERETiqfuHFA0BXjWzj4B3aKhR9LyZ3WVm5zS2ucnM\nFpjZh8BNwBXd9n76sZdeeikrPz//sm9961tTZs+efcS3vvWtKfn5+Ze99NJLWV3p9+OPP04vLS0t\nuvrqq8sAIpEIH3744dCZM2cuA7jqqqsWvfTSS8UAL7/8cvFVV121CGDmzJnLPvzww2GRSKSL76x1\nqlEkIiIiIiIiEkedqFHUTn/+EXBoC8fvaPbz7cDt3Xrhfq66ujpw/vnnT6uqqkppOrZr164AkHj+\n+edPKy8vfzQtLa1TGZvrrrvu2LvvvvtfW7duTQRYtWpVSlpaWl1ycrIDjB07dufmzZvTATZv3px+\n4IEH7gRITk72tLS0ulWrVqUUFxfv6vKbbIFGFImIiIiIiIjEkXvHNukZDz74YFEoFEpo6VwoFEp4\n8MEHizrT789//vMRWVlZNWeddVZF1yKMjXZHFJnZwe4+Px7BiIiIiIiIiPRnTvePKJLYWLJkSUZd\nXV2LiaK6urqEpUuXZnSm39dff73gzTffLMrNzR1RV1eXsGvXrsQrrrji2Orq6qTa2lpLTk72xYsX\np2dnZ+8EyM7O3rlo0aL0CRMm7KytrbXq6uqkESNGxGQ0EUQ3oujnZva2mX3FzDJjFYiIiIiIiIhI\nv+eAW8c26RFjxozZnpSUFG7pXFJSUnj06NHbO9Pv448//vaWLVv+d9OmTY898MADrxx88MHrSktL\n/3bIIYesmzVr1gEADz/88IGnnHLKCoCTTz555cMPP3wgwKxZsw6YOHHiukAgdhPE2u3Z3U8ALgUK\ngXfN7DEzOyVmEYmIiIiIiIj0Y5p61jfccMMNK4PBYIuJomAwGL7hhhtWduf17rvvvrceeuihQ/Lz\n82ds27Yt+c477ywDuOOOO8q2bduWnJ+fP+Ohhx46ZNasWW9153X3FlUxa3dfYmbfBuYB9wOHmpkB\n33T3Z2IZoIiIiIiIiEi/ouRPn5CWlhZ5+umnnz///POnhUKhhLq6uoSkpKRwMBgMP/300893tpB1\nc5dddtn6yy67bD3AEUccsWPp0qXP7t0mIyMj/I9//OOVrl4rWtHUKDoEuBI4C3gZONvd3zOzocA/\nASWKRERERERERKJiqlHUh5x66qlbysvLH33wwQeLli5dmjF69OjtN9xww8ruSBL1VtGMKPop8BAN\no4dqmg66+7rGUUYiIiIiIiIiEi2NKOpT0tLSIrfccsvyno4jXqJJFJ0F1Lh7GMDMAkCKu1e7++9j\nGp2IiIiIiIhIf+Ja9Ux6t2jKZL8CpDbbT2s8JiIiIiIiIiId5R3cROIomhFFKe5e1bTj7lVmlhbD\nmERERERERET6MY0okt4rmhFFO83ssKYdMzscqGmj/W5mdrqZLTKzpWZ2WyttvmhmC81sgZk9Fl3Y\nIiIiIiIiIiLS3aIZUXQz8KSZraMh7VkAXNTei8wsAXgAOAVYA7xjZnPcfWGzNmOA24Hj3H2rmeV1\n4j2IiIiIiIiI9B2aTia9WLuJInd/x8xKgAMbDy1y9/oo+j4SWOruywDM7AngC8DCZm2uBR5w962N\n19rYkeBFRERERET2Z288/xZ3nfsjvBMLdR857VC+N+eb3R+UtE+JIunFohlRBA1JonFACnCYmeHu\nv2vnNcOA1c321wBH7dVmLICZ/QNIAL7j7i/u3ZGZXQdcB5Cfn09paWmUYYuItKyqqkp/S0RERKRd\nvfEzw6cfriAS+iwzdOE9Z3S6r1/9+OHdPxeOH0pKSkqXYpMoOKBVz2LBa2trLTk5WWm4dtTW1hpt\npCvbTRQb3tvHAAAgAElEQVSZ2Z3AFBoSRXOBM4A3gPYSRdEIAmMa+x8OvGZmB7v7tuaN3H02MBtg\n8uTJPmXKlG64tIjsz0pLS9HfEhEREWlPb/nMMD33CnZs3hm36z2+8Rfk5OTE7Xr7G1cqo9tVV1cv\neOSRR0quvPLK9UoWta62ttYeeeSRIdXV1QtaaxPNiKILgInA++5+pZnlA49G8bq1QGGz/eGNx5pb\nA7zVOJVtuZktpiFx9E4U/YuIiIiIiPRrpyZ+EQ/H/5n34rwvA/DThf9NSUlJ3K/f7ymN0e1WrFjx\n/XvuueebP/3pT8ejZeXa4tXV1QtWrFjx/dYaRJMoqnH3iJmFzCwD2MieCaDWvAOMMbORNCSIZgCX\n7NXmT8DFwCNmlkPDVLRlUfQtIiIiItLrVFRU8I0T/5vVZeuial88oZAf/O2bGrkh+zh38JfYWRnV\nYtMxdeO4/wLaHmH02r/KePCxN9i8ZScRdwYPTGXGFyYz/YzD4xlq39IHpp6ZWTJwPlBMs9yBu9/V\nUzG1pbH28X/0dBz9QTSJonlmNgj4FfAuUAX8s70XuXvIzL4G/IWG+kMPu/sCM7sLmOfucxrPnWpm\nC4Ew8B/uvrmT70VEREREJO6+cfpdvP/S/E69dsXHq3eP3AAVF5au3U+xdHHel6kblEZ46sHttq3Z\ntYOfPPQqP3no1X3O5WWn88zsL7fwKjj8lz9ja21tl2PtC6xvjCj6P6CShjzA/vEfRoB2EkVmZsAP\nGmsGPWhmLwIZ7v5RNJ27+1wa6ho1P3ZHs58duKVxExERERHpE954/i2+e86Pur3ft59/n1MCFwJw\n55xbOX7a3mvBSH/W9N++N4mkJRMpGER4yGAiOQO73N/GzTs5/vyG/98JGWw8Bkjs/aNrupXTV6ae\nDXf303s6CIm/NhNF7u5mNhc4uHF/RTyCEhERERHpjf7woz/x0Df+Ny7XakpEXfPDS7no1nPjck3p\nGWVlZbunePUGHkwgPCKHUHEunpne/f0bVBfAzqHsf0kiAKxPTD0D3mxcbKr3DXGTmIpm6tl7ZnaE\nu6vAtIiIiPQ5ZWVl3HbM91ut9XHIlIP48d96ZbkF6UVefuxVfnjZz2PWvycFiQxMxQem4ANSiKSn\nQHIibsbP5n7IT/+2EFISwfZ8uDzxqFF8/xvnxSwuib1/P+kOPir9pKfDAMCDAUKjhxAaPQQSE7q/\nf6AmH3YUQzilTyRKYqcXjygys/k0RBgErjSzZTRMPTMaxpMc0pPxSexFkyg6CrjUzFYCO9HNISIi\nIr1YNPU93Awf0PBA/u7abXzuiP/EU5PxxAQ8JQmSg/s8kB9xSCGz7rwolqFLL3Vq8EI80v39RtKS\nCA/NIjI0i0jWgH3uuWi89tanu6fxjCzM4vf3XRX1a+/46C5W7lrZ6vkEEriy+HJOyDuuw3FJdKYN\nvJTanXU9HQYeMMLFedSXDIPkxO7vH6jNhu3FEBqwnyeImvTiRBEwracDkJ4VTaLotJhHISIi+4UH\nv/Fb/u/+PxOqC+9zzhKMg08o0cgO6ZT7b/wVzz3wUpttPCFAJC+T8NAswgWDICmaj0Gfeeej1bsf\nyLMGpTLn11/tdLzSN1RUVOxRaLo7eMAID88mNDIfzxrQrX0vX71l9z360/85l0MPGr373JKKT/mf\nZa2uhNyiMGEeWvEwD614ePexnGA2Pz7sh90T8H7u9OSLCNfHIAPZQeH8QdRPLMLTU2LTfxJsGwu1\n2UoQ7aEXJ4rcfSWAmf3e3f+t+Tkz+z3wby2+UPqNaD4h9eJbWEREerPzsi6nalt1u+08YHgwyAfv\nLOPzqRdDXYiAgwXgsQ2tL8crEk2CKJKRRmh0AeFhWRDsnqkUW7bV7H4gf+h7V1BSonu0v4nm3uoI\nDxjhkfnUjx3aMIWsu/o1CKVCKK3hf8Mp4Alw6WN/oj7NyRxWRUH+VgZlVJPQxdvfcNzX8KMPz2VE\n8k6OT9tAQVLzhZBGQvB/CehvdrumDby0x5NEnhCgfmIx4aLcmF2jJhu2lYAHlSTag9NXahSNb75j\nZgnA4T0Ui8RRNImiF2i4lQ1IAUYCi9jrphEREYG2VwJyMzwzlUjWQCJZA4gMTsdTkyEhsG/j+hC2\ns5bpZ9yL1YeJDEzi2WdvUdJIgOgKCkcGplJ/0DAiw7I7fR0HIokQTqbhk1AjC0NwF1gErvnWbwD4\n0y+v0P3ZT3Tn8uRuRrg4l/oDh0FqUrf0GUqBXTkNW10GLUxZc3KztzN86GYGpO/q9HWyEmo5PK2C\nMcmVFARryA/uIinQVnJjOYSOJbKh+bHjCRQ83NoL9kvfOP2uHp9uFklPpu6osXhmWkz6dxrqEFUV\n9YlkSI+wXjwcw8xuB74JpJrZdj77F7AOmN1jgUnctJsocveDm++b2WHAV2IWkYiI9EmtPbg74IPT\nCY3M79iIjsQgPiiID/pstZVzr/wVGek1fPG0hXzpjEVAIQT/nUDO57vnTUifcEbqxYRqQ62e96Qg\n9QePIFyY0+GaL+Ek2JUNdZmfjdJo65vwhF1OQg0k7oTTvvkbgpXw1h9u7dA1pXfpziRRJC2JuiPG\ndMsUs0hCQxHg6gKoH9jaPelkZ+2gaPgm0tNqW2nTtkEJtRybvpHDUys4ILmq8wHv9gaRDWMbfgyc\nTSDvx93QZ99VVlbWbfdXZ4XzM6mbPLrD02+jFUmArQdpqlm7ujlRZGYpwGtAMg3P+U+5+517tUkG\nfkfDqKDNwEUtrWzu7j8AfmBmP3D327s3UukLOvzXwd3fM7OjYhGMiIj0TacELtznmBuER+QSGpmH\nD+7oQ5KTllPDuIM2MGHERg4aWsGonK1kpe0iGGj+yWophL6817fXAAMh+BdNf+iHWrrXmgtnD6Tu\niNEdGrkRTmx4AK/JgfoWR2e0xknICJGU3ZC0qgdCIefQ+/+bKkvgF6dM5+SSkqjjkJ5354U/6raH\n+PDQwdQdekCXH8YjQagqbFhGvK2kZVpqLWNHrWXggM6NIBqZtIMzMlZzWOpmEmL1fB95jsiG54DB\nBAreitFFercbx/1Xj14/NDKP+onFnSqcHo1wImw+GEKtJjMlhmqBk9y9yswSgTfM7M/u/q9mba4G\ntrr7aDObAdwDtLVKwzfNbDpwPA2prdfd/U+xegPSe7T7L5eZ3dJsNwAcBqyLWUQiItJntPbNe2RQ\nOnWHjtxjNFBLIu7UDnZ2DncGFu0kY3A1x+WtZVr+SialbybY6XHZO5pNfygiUPByJ/uR3uKjNz7h\n30+8o9XzDoQOHErooOFRPwCFkqGqCKrzgUD7r0lOriMnawcDB9SQmlJLakodCQkt36Pu8KtNs5i9\n0UhOqicQgBsP+CqTcw6LKjaJv7KyMt58uuvJCwdCE0YQGjOkS/1EEqBqOOwc3l59Fyc/t5JRxetb\nvR/bUphYxXmZKzk0bUvng+2wrY2jjIYQKPh7HK/bs9pLdMda/egCQgcXxaz/cBA2T4RQupJEPcHd\nHWgaBpjYuO39R+ELwHcaf34K+JmZWeNrW/IAMBp4vHH/BjM7xd21mkM/F81XHAOb/RyioWbR07EJ\nR0RE+oozUmbss3qZJwSoHzec8KiCVh/WI+4NS+SONhgQIT9nOyVDNnFm7iqmDlhPQWJNN0e6cve0\nh0DB4m7uW+Kh3SSRGfWTRxEeHl0tonBSwxLNNQW0m1RKTq4jL6eSnKwdUdV6SbEQ+cFd5CdWk5+5\ni7TAZ1PkKrbcwstbnSQLcXjyZtJSv0Ig56aoYpbY646RHg7UHz6K8IiujWasHQRbSyCS3Pb9GQhE\nGD1yPfm5lR2+RnqgnssGf8rR6Zs6G2Y3WN/w9zn4i34/hfhb53RsxbnuFirOjWmSyAOw5WAliTqi\nE9+F5ZjZvGb7s919j3pBjcWm36UhufOAu++d/R4GrAZw95CZVQLZQEUr1zwJOKgpkWRmvwUWdDhy\n6XOiqVH03XgEIiIifUdL34pGBqZSd/RYfEDLy+tGAs72YqgeaiQkRSgavpGCvK0cnL6Fy7OWkhds\n+yH80+oM/rptKOsjqWwliRpPICdhF7mJu5iUuoVj0jaS3GaRVRoeSGwKgXzVYewrokkS1R0xKrqC\n1e5sHwlVI6zdBFFaai0jhm8iJ2t7m00NZ3TydianVjApbUu79/Ee4dT/jPD6nzX2H4DgG5ou2UO6\nY6RHdySJdhcAHkG792gwIcz4g1aS0YmpZqOTtnNDThnZwc7VMep2oS8T2ZBNoOCfPR1JzLz9/Ps9\ndu1wTgb1E0fGrH8Hto2B+gwliTqk46ueVbj75Da7dA8Dk8xsEPCsmU1w9487GyKwFBgBrGzcL2w8\nJv1cNFPPXgYudPdtjfuDgSfc/bRYByciIr1PSw9U4dwM6o45sOXVy9ypLHZ2FgXAICdrB6OKN5Cd\nUs1Fg5dzXPrGVq8VdlixI5MHP5nEsyvGgjX0n5RYT0H+VnbkbWNdUjof1mTzx60jOS69nJMGrmdI\nW6OSvJTIhrEaXdRHtJ0kIuokUTjgbDzK8KS2P5inJNcxYvgm8nIq23xOLwhWc9LA9RyZtonMhPp2\nr9+SPfuPNJsuuX9Nx+lpXznyP7vcRwSoO3Ecnj2w3batCQdh63ioG9T+w2MwIczB41Z2YkUz58yM\nNUzPXBG7OkSdtrnf/m2enntFj107kppE3ZGjo5pe21nVQ6GmoNfdUL2b0+3FrPfo3n2bmb0KnA40\nTxStpSHZs8bMgkAmDUWtWzMQ+MTM3qYh4iOBeWY2p/E658Qiful50Uw9y21KEgG4+1Yzy4thTCIi\n0ku1lCSqHzuU0Li96sI0TnWvyg6xfUIiWICkxHpGH7Ce7MFVjEmu5Ks5n7T6gB32hiHZ8ysyuaB0\nRsNB+6zvurogq9bksXptLtmDdzAkfwtkVvNK1TBeqRrKQcmVTB24rs2irA3THe4kkHNpZ38dEmPt\njfIIjStsP0nkTnVOhG3jE9oZoeEMLdjCyBHlBFrIdzbJD9bwhcyVHJW2KYbPXY3TcRhJoOAvsbqI\nNFoyb1mX+6g75RB8QGqnXx9OgoqJEE6LXZIogHN19mKObSM53xv0x2TRjs07e+S6HjDqjhwDyYkx\nu0ZtBlSOiln3/Vv3r3qWC9Q3JolSgVNoKFbd3BzgcuCfwAXA39qoTwTQ+rc10q9FkygKm9kId18F\nYGZFxDT/KSIivdHZGZftc6x2fCGRMUP2TRKt2MC6f8uHQCKYMSC9hvEHriIpKcwRaZu4NnsRiXtN\nzndv/IItAsc9fT4VNEzfSAvWc8jgjRyaU87IgZUMStrF9949jJU1uXg9VGzJoGJLBmmptRTkbSU/\ndxufMIhPagcxKKGWzw3YwOfSNzA4WLfvmwp9l8iGvxIoeLg7f1XSDc5IvbjN86GCTEJjh7beoPFz\n76ZxIepzE9tMEgWDIcaOWkf24NaXAs8I1HH+oBUcl14ex5EYyxsTmlcTyOn6qBfZ1yXFN3S5j5oz\nJ0FS9Kvs7a0jSSKzCOMOXNWpJNH12WUcmd5aGZLepT8li05PbmtBqdgKjSvEszq66mj0IgHYVkJM\nRyv1Z51er6N1Q4DfNtYpCgB/dPfnzewuYJ67zwF+DfzezJYCW4AZbXXo7n9vfP4f4+6vNCaggu6+\no9ujl14lmkTRt2hYWu/vNHyfewJwXUyjEhGRXmXOL19kV9WetSzqh2fvmSRyh0iE1Dnz+PQnRzYc\nNyNr8A5KRq8hIcE5deBaLh6857f3YW/4NLNtVwJHPHcNAQ9TmLaNL436J6eOWMHI9O37PJifNHQV\nAGUV8MX7LwALUH3QIJatLGDF6jzyciobRhmlw/9VFvF8ZSGHpm7mpIHrOShl76KvbxDZcDyBgje6\n8TcmXTHnly8Sqg21ej6SkkT9UWNb78AdwmHWneCQ2HaSaEB6DePGriY5ufXrjUveynU5izo9xazL\nQr8msuG3BAoW9sz1+7FNq9qacdG+muMPbEgSdXKp8XBi9EkigJEjNpKZ0bGC/30tSdSkvySLwvVt\n186L2XWzBhAaXRDTa+wYCeFUJYk6rZsTRe7+EXBoC8fvaPbzLiDqomxmdi0Nz/5ZwChgOPAg0L+r\nz0tUxaxfNLPDgKMbD93s7n3rXxoREemSn37513vsR1KChCaP2idJtHPOPNbNOmr38dzsSg4cvRYz\nOC69fI8kkXvD9oN5k/jtyiPJzdnG2Ud8wLm5Kzg6bVO7hakBSnLgo7ue4rShB1Oflcr2Y/PYflwB\nGyKD2bBxEAMH1DA0fyu5OZXMq8llXk0uwxJ3cmPOQvITm38jv5FI+dcI5P+sy78r6bq977fmnIZa\nMK3OD3OHUIh1xwUhse2i1QPSazh43EqCCS3fawGcczNXclbG6k59YR6OGGWVWZTXpZGcWM/IAdsZ\nmlTd8Y4ACPWbB+fe4t9Gf6XrneRkdjpJ5AZbJkSfJMoavINhQzq6hL3zb4OX9rkkUZO+fs9/cfi1\nPXLdCFB31JhO35vRCKXAzjYGdUoU+sYcna/SUJfoLQB3X6IyNPuHaIpZn0fD3MXnG/cHmdm57v6n\nmEcnIiI97vTkfUcl135+4j4fQFPnzGPdfUfv3s/M2MnYUQ1JonHJW7kia8nuc40DPhj3p2s4oKic\n449YyGmZazkvc2WbCaJt4UTm12RRHkqhJhIkJ7iLoqQq/vej+Vx6yCFkP7+awS+uZevFE6icmMqO\nqjQWVaWxdkMWE0pWkZgYZm19Ot9efzg35S7g4NRtn3XuLxGp+Gu/X6K5t3vwG79t83x4yCA8Pbn1\nBu5UDA5CSntJomomjl9JINDyJ/UkC3NjzkImNL9H2rGrOsAn76bx8UeZvB8+hAUr8tmemNRsNSDn\nwCHlfH70Usalb2NM8naKkqoIdmD+QcNUtDkEckqifo20bMOyri0LX3P24V16EN82NvpVopKT6hk7\nam2Hr3FCejlTBm7o8OvaU16fwpLaDFbUDSDZIhyQvIOxyZUMTGh9ZF5nRTaUECgo6/Z+42Hruuj/\nfnSn2nMmQ0JCTK+xo5iYTTkbnFnF8pj03HuYx2TqWSzUunudNf6tayyA3Tcily6JZurZne7+bNNO\nY3GsOwElikRE+rmysjLC9eE9joWGZUFSs38+3OG1t/m0WZIoLXUX48auJhCA3GANX8v9ZPfDsDvU\nhuDUNy/gmMmLGJ68k2uzF1Oc1Hp9mE2hZBZUD+KhDyawqyqF5KE1JGV9Ng0oL1gD1AJGIBRh8P8t\nISl8CFvGOaEBRtXOVOZ9MJpJBy8jNaWeEAF+smkCdxa8T3FSsyKjoS8Dfffb6/7g6R893+q5CFA/\neXTrL3YHM+oOaztJlJ5ew6QJK1ptkmRhbs5d0MI0xX3tqjH+/Gg2rz47mKUfpxIONXRaNyFIeEwS\nibWQ8z5UD3W2HwCL1hfw6cZcikeUMyR/G0kWZlTSdk4cUM6R0RbIDp1DpELJoq4oK+ta4qEGuvQg\nXlncsVWiRhZtIDHYsSlMQ4M7uXTwpx2MrGVLKgfzRvlw3q0o4L3N+SQOqqFo+CYSEhqfF3cAOAXB\nGsYkb2dM8nYOTd3MgG5JHEWIbPomgdzvd0Nf/V9NFq2PuOwm9elQE7MxJU7xiHLei1X3vYn3iWl7\nfzezbwKpZnYK8BXguR6OSeIgmkRRS39ponmdiIj0cTeO+6899j1g1B92wJ6N3Fl3x9G7H9IDFuGg\nsWsIBiOAc0XWElID4ebNuX7tCYwaWc6ElK18JeeTPc43CbkRihgJHiE3WMuUjHKmnFgOwNm3nUMk\nMUT6CWHSxlazMZRK4Lh6Iv9oeG1gew1Jq7eTU5/BpsOccKoRCifwwcfFHHno0saHG+Oe8oO5b9jb\ne4xi6utTHfqy3373iTbPh0flQ7Dth/N1J7T9wTsQCHPohOVtJolm5i6gJIokUSgEXz55LOuWp+xz\nLvGTNYSLciEpiAHp6yBxB2ye6IRIYOnyoWzcNIjRB6znE0/gk9rBvLxjKJdnLWVEUhQrJIXOIVLx\nJoGcnPbbyj72/tvWYedM7vRooi0jYNeI6Nunp+0iN7tjdWOTLMyXc8qimsLblr+vL+R7HxzDsh2D\n9zxRk07F5gxGFW8gO6spyW9sCKWxIZTG6zsbauMckrKFLw5azrBOT7lsFH4K6FuJohkjeqik6wlH\nxHTKGcD2kcTsGrnZ2xmQXtt+w/6gb4zLuQ24GpgPXA/MBR7q0YgkLqJJN88zs5+Y2ajG7SfAu7EO\nTEREep/QgUP3fFB3Z6ftaviw0/ihsXD4JtJSG1YYOy59I+OaPXC7w1WrTyAQgOPTN3Bz7sf7JIlq\nIgmEw+A4KQkRElv4auK5u+cw+MUlbL2ugtX/k8fWN7PJuWvPB5mEZeUEQjB4IRBp+DQWCiXycdln\nT2i7PJFfbd63KHKkQp+BesKj3326zfOhCUWtn3SnYqA1fLJp4wHm6MMXt3Ha+XpOdEkigGAQ1i1v\nedlpC0dIfG/Pwu1JOyCzWQ5ye1Ua788/gOWr8ohEYFldBt/dcCiPbx3JrkgUH9FCx0YVp8RAF0Zs\n7CqmQw/ZRcM7vpz9mRmrGd6F5My6nQO44Y3TuPr1M/dNEjWqrUti4eJCFi4aTjjc8vv5aFcWd244\nlCe3FlMbzT3dhsiGNgrY90Kb12yN+zXroWHIbiyvkQa12bFJEpk5RYUdv9/7qqbpZ9FuPcHdIzTM\nJPqKu1/g7r9y976R4pIuieYv9o1AHfCHxq2WhqJWIiLSj52RsmdtIjcIjRqyT7vKKekNa2ICaWm7\nKBzasIrQwEAdMwbt+aDcOOiIw1MruDJryR6rmdWEA4RCsDMSJiGhoQ5xS97ZVMBvPpnA6b8PEqir\nJ2HuUrb9IcjKX+w5JSlh3VbYVU9SFQxY89nx7TvSqdr5WY2b92pyqArvlY0K/bDli0uPCRVmt14P\no2nK2aG0+QA+5oA1n02VacH5mSsYlxpdkqjJrJdWt3ouuH4r7NxzGfPUjZC09bMY3I0163JYsGgE\nkQhEMF7aMZxvrT+c96qz271+X3t47g0qKrpW2Llm2mGdHk2x7uj22zQ3IL2m2YidKF8TqOfUtI7X\nMwIIRYxfLzqE0//yRV5ZVxzFK4zNWzNYUFbYaoswAebuKOTb6w/nw5qWk07RinTxv11/Z/mDIDUp\npteo3vdjQLcpyNtKakoPrS7ZE7yDWxxZg++YWQWwCFhkZpvM7I72Xiv9Q7uJInff6e63ufvkxu12\nd49iTDSY2elmtsjMlprZbS2cv6LxhvugcbumM29CRES6X6huz5E+4SGDIXHPaT87hoYbHtIBcMaM\nXL/7+Wl65so96lO4w2NrhjIqaTvXZS/a/czfuGAac3cUEgxCTgsjiKrqE/ndkvGc/uIXufjVL/A/\n84/jOx+dSAjDgKT3lmHb66g576jdn6XMneCKhm8m09bTLE5Yueaz4gqOMXf78H2uGdlwRLu/I+k+\nd174ozbP7zPlsTkzKnLYnbBsSUKgnvzc7a2eH5VYyVkZa1o9v7cd2xL4f98Yzi2nFdHW5J6kN8v2\n+HxvwKDF7B7l1mRb5QCWr8rfvb8lnMJPK8bxwKaSdkdiRDacFnXcApcN6+L3ncEuVGBIokNJpqEF\nHV3lDE5PX01qYsennC1Zk821957HfS8dTXWo5ZFyrancMYCtlWlttqkIp3DfpvE8sKmEHXsn56PV\nR0bRdbUGVmdFRsR2KqoDNbmx670z93uf1cHRRD0womgmcBxwhLtnuXsWcBRwnJnNjHs0EnftJorM\nLNfM7jWzuWb2t6YtitclAA8AZwDjgIvNbFwLTf/g7pMaN431FxHpBVr6xj1UMmzPA+7sGBPc/dCT\nPXgHGQNrAEizeo5J33f4+L8CI/ha7kKSGutmNK1+9ueKIs4ftHKf9itWpHLPzw/kpB+fzV3vH8/S\n7Xt+G73y26MbZr3VhwkuXtfQZ7PzCSs2gjvBXZDc7PPnlq0DqK377EHl9Z0FhPYpKtmxkSXSNW8+\n/VbbDVp7uG5MANYd1EYb4LBDWq9LlGgh/iP/46if3xfOS+OaEw9k7qPZuBsBWv+yN1BV25AJbSa4\nCwbue7uzbkMWVTv3rHc0ryaX+zaNb+H+bK6/rw/UvcL1na/bU9PJ1wUWrIJ3P+7Qa4LBELnZrSc3\nWzIwUMfn09d36DUAG7YM4Ov3TePTVTkMWgTBnR1/Ki1bMoz2J6QY82py+fHGCYQ7+eDbF0YV3Tzx\nO3G/pgeMcP6gmF6jfiBEkmMz7SxjYPXuaev7jV48ogj4N+Bid9/9D4y7LwMuA74U92gk7qKZeva/\nQBkwEvgusAJ4J4rXHQksdfdl7l4HPAF8oZNxiohIHF016uY99j0lEc9M3+NYdY7v8WA+JP+zegzH\nDdi4RxFVd9hRBxcNWs6ghPo9jv9u5XjOylu5xzH3hkLBt2zN52//E2Tw/Usp/P4HZJauJ1DTrP5C\nTs7uz04JqzdDfYjag4bvPhaoqSOwviGu9HXNozfWb/gs6VQVSeTt6n2/iY1U/G9Lvx6Js7rhg1tP\nAplRAW2OJkpMrCU5ufW6HRdkruhQ0d8ffn0I2yqiG3FhQKB836TjgNUQrN77k7+xZNmQfR62y2oH\n8ecWRr01pylocXL24R2edhZYu4XkxevZOSm7Q6/NydpBINCxp8MTw+tISep4ImzWH46jqqZhSq45\nZC7pcBeEQomsXR/d1LKV9QN5ecew9hu2eKHeP6po79VC4yGSk7HPqN/uVpfQxaLkbcjJ6ljB9n6h\ndyeK/j979x4fV13nf/z1PTOT+6Vpkya9l7ZAL9BSKQW5WUAQERAFL1xUdlXUnygq6O6iP131B7ur\nooviqoh4Q0GRFZFlYXG1i6yAQLmXSy/0fknSJs19kpn5/P6YNJlJZiZz0sxkmryfj8fsZuZ8zznf\nxMP0fD/n8/18Q2Y2LCprZk2Av5RDOSy5kWpROeeeNrPjnXPPm9ny/s+eNLOMOfnOuUuAc83sQ/3v\n3+dUPQMAACAASURBVAecaGZXJ7S5EvgnoIn4esSfNrNhk/2dc1cBVwHU19cff9ddmVdFEREZSUdH\nBxUVFePdjYK14ekhtYWKglhZcdJnfRUMDM6dZ5SXDa5SMivUScglDFYM9kRKaQh1J33WEQlSEYok\nfWbAtr4Kqr1eqgJ9eM5oaQzQvDt+fnMQKwsSqS6CgCPQ1EOgJ4oDYqVFUBzCtXYOxA0sFMDK41ka\nfeUMPCJxrr/P/Q2LXZQZoRQ3wcFjRv6DySEbes0lilWXZRxgJ16LqZSX96SPM2HMC3Vk3D+VDc9l\nnmaTKPEaTBQLQrR0ePvi4j5CoeSBpsOYFeoi6DIFARYf2rSoSSLTtTaS2JTykRsN4Q50AUbvzLL0\ndbZSKCnp7V89MnuzvQ6CQX8jyvauYnY1VQ37PFoCsVEMByvKe7L67ym7azqNPH8v+71nOJRrbLQO\n/vuXM70RIlUeFjq0ouTplJWFkwKjf/O2K582s1U5OVkBKJk1x+Z99DO+9nnti5/J29/EObfOzN7g\nd5tMHNncTRx89LvbOfc2YBcwdYzO/3vgTjMLO+c+AvwUOHNoIzO7FbgVYNWqVbZmzZoxOr2ITFZr\n165F3yXp3XDmd5Pe95y+FJtWOfA+EjQaT3YDg/cF8/YwqzY+t2thURvvb3guaf9oFBpjpcxICBQd\nfE5xcABvBt198K8ty/mbqRuS2gLc9Jk6/uuuGRwcgUSdo/2KVXTO9Zh5wxPxQFFFCeGzV+Due4Li\naLylAeG3HIeVFdM+B9oXDI5gjl60g+m1B6d2GF9pWMecISsFeQ2vIbnV3Nw87JpL1H3R6oyBol2n\nk3a7cxFOWZ1+pbNP1b7IijL/qxPdcM5yso0uRYHed5yYclvzCuidknycQCDK8Ss2UVyUnAV1TEkL\n107PPH1J1+vIMl1rIxnpWhzKa2qj+NGX6Vpcze6PLsl6v0AgyknHv+prcbUjNu3j/Wesz34HoKsn\nxPu++i6aWodnvcWCsPdEsKC/KOrshj0cMT+7WjPHle7jmjp/fQYgcAle3Y3+9xslv/cMh3KNjUbi\nv3O54O1rx3tuI9u+nJvYQFlpD8fPz39wTTJa4ZxLNffVAcOffMiEk80/P//POVcNXAtcB9xGvLjV\nSHYCiUsgzO7/bICZ7TOzg4+gbwOOz+K4IiKSZzY1+Unq/qUkDZamJaSMH1mcfF9hBr/YVz8s8NMX\nSR5vmcFvOxbw99OfH9YW4OP/so/E3OuAGWVP76ZmPRwcTnsdPXiNB7ALBwflDghs3AP0F7VOKCK8\ne2/icw/HH1JMhYg1j09R0snkY8f+Xc6OvXpl+iBR0CIsLx3tEtbZZ0Fkutkq3TP8s2g0wOYtDcM+\nf7Gnhic6c1usVtIbTX0ib0/8+upY7u8Z69QpHb6CRADHtTb52wG47feraGpNnSnjRaBii+9DsmNP\n/ciN+j3bPY2ns1jdb5job/zvM5GVFOUsSEQ0RmjdZnrn+s+my1Zd6SScdlbgzCxgZlUpXpVmpqln\nk0A2q57db2YHzOxFMzvDzI43s/uyOPaTwJHOuSOcc0XAe4Gk/ZxziQssXgi87KfzIiKSe7Gho2wz\nIgkZEEWhPkqKB+sOLUoRKFpe2Tfss6JQ8vv9nXD51M3DZmYcXBXtQycfSQwvaZp+aOMenBmRhNXO\ngq/vjfc7oV1wWxOYEYjElyc/qK29lO6ewY483lVH39CiwZF3ILnVutdfwd5Eu04iY4ZHKMPt7LK7\nd6bdtaW9hMeeTb9vTf3YFF0tTlMzvXl/Jd3dwzt/Z8tCumPp65DE9qRaN0TGxJplvusTBfa1Yw46\nj/UXKKoq81cLJrYvyhtPbPW1z86mSv79f5b1vzNK53dQuaKFimWtBPq/s8t3pt8/Y398zCb7ZcvC\n0Z1EBsSmZD8V1q/gxt14HT2EZ+UuUDS9YZIuHlHYNYpkksvNJFPAzCLA1cBDxANAvzazl5xzX3HO\nXdjf7JPOuZecc88BnwSuzFV/RERkdGLTq4an/iSorEx8zm4clTDyNYOtXUUsL0mehhCLwRfeP5O3\nzDyGtx+5GIDaSpL2O7gi2ue2LebSH55I084SPJIn+7jeyECh4IEC1rtbobuX3pLB2dWuLwo98YH9\n0KLWO3cPDuB6LcC6YU+381+UVHzIOIk+/f92kW1RVi1JPzj5/A/O4e9++OG02+965jXG4s490ENS\nltsgx97m4SsYHYgVcU/rvAxHTF+0Ww5Rpc/ZFpEorrWL8OxyYpX+HsCXR/pGbpRg1o426mb62+fO\nP6wgZh5VK/cz96ObmPme7Uw/dw/15+9m3kc3Uv+O7RRN6YVev9e5Y9ee7Ov57I8WjyqrKNb8O9/7\nTFSxihzNBDIj8Hr86Urf1BxlLBGjuHKSrXYGkLDsfbYvkXzKWaAIwMweMLOjzGyhmd3Q/9kXD2Yk\nmdk/mNkyM1vRn62k/H4RkXH28C//lPQ+Vptc5PRAXTgpcFRd1Tnw89RAmMpA8kD1T+F5SVlCZuB5\n8OQfaoEAPZ1FvG3+0fzioYWcd+2lNDXFA0R/u/1UPrTzZD5S/gqtX04/6SP4avyR98EH2M6M4JZG\nOGFe0jDetcaf0IfaIdgxuKWxaUpS7Ot3BxJnTUvBy5DgMXd2Y/oEkJd6WXVm6kymFzbV8+LrDYDj\nzE9+IGWb5t1jUzTaAV6aMVLrgdRP8P/YMXN45pvkns+5YF5LJ86MvoYUFcszMaOk3F/QZ06X/6k7\nf3hqEdPO3EvdOXsJVSefz3lQcVQHs6/Ywuxg6uXoj6hs5ayZW3hTwzamFid/R7++bfjUyUz+3JH9\ndLUBkev97zNB5aw2UVMbXnf8CyqSo0BRRUWX30S9iUMZRVLAtDSGiIgkefS3Tye9j1UnDHLM6Dw6\n+WZxanXHwM/Tg8mDhWg09VS0z75nCoMjfEe4aAo/uC9eHPjif/wwy//uGX4899HBnVwE0kyJ9/Z3\nQLiPvtIQwe74YCe4pZHI4pXApsF2rZ1EZ9TggPKdxoGj+4tixwK0d5RQVdkDwO5IBb0xR5HPZall\nnGQYYMysTz8VZ2m0hZLS1P8bP/bS3IGfI9H4rdKiX3+IxOdrxe0dzOYlf31NI9ANsRQJAR2dJZgN\nn+1kOF7uqWZ5aerfL7bnOLyGDPPmJC+8ffHgTW+dv2yPUIcRmuIvw6Le+aug9Nq2aTAzzJQTMhed\nDpRHmXv8PnY8Uzfw2Sn1O/j4knWsnr574LPeqMdDO4/gX188ga0d1fgdYmwIV6W81jPzF0ybyKy0\nKCfH9ZoGsy4jtbnJWqqdNkmnnYGCP1LQRnw04pz7QsLPuco5FBGRAnHpV89Peu/CQ6ayBJLTg4oT\n6hPNC3UkbuIfdi0ZNhWtJwI33d3K4B2S0Xfq0SQGjs7auDfplD9/Zj2Jd1Qf/sfXuWPdc/2tIbC1\naSBIBOB6+rAhx4h1Dg68Shtd0hS67buSpz00RbSgx0SQaZnw5fXpBycvbDqYDWFMn9LKol+/DwYm\nPsZf4YoKYj6WOs+kKE08y8yjry91PaL1PTUZjuivvo3khjsQz7bsq/OXUVTSFcPzscR9rMuYWdPj\n6xzPb26g+vjsCrkHimNMnRIP+L9j3qvcftoDSUEigKJAjAvmbuI3Z/2WxdX7yHZFwIO6LERLNDfB\njsnASnOUUdQcD3YaEPE5fTJbdTWjr1F3OHNo6pkUtrSBIufc3znn3ghckvDxY7nvkoiIjKfFixcn\nvXftQwcgyQOAxCfAs0LJA9Q2V0NDKHn/0v57zXPeuxuIMqW2h9oZbSQGjr7RfhKLfn0Vi359FUt+\nfSV/17KKhr9UUv+/5fz6hee4+MNt1DXAgzvjwaLgKzuH/YMWeHFrUk9t+uAUOm9ITZiW1jISA1F7\n+xQoyqs8TzuItsY4Yl7qYEpfxOPlrQezJ4xffeUeoJRhnXSO1//vsQCUnBak9pEyik4OQIqxlMPI\nVC+peFv6vnZ2px4AvtJTnX4nKQh9c2vjA+xp/gbxxT5nkfW9FGXGUf6ya17cOp3SIzpHbthv1oz9\nHFHZyo0nPEIgQ7ZlTXGY753yEJ7zX9ttW2/uiiVPeEXpC9wfCq+rf3FqzyU/JBojLmqEQpM4AqKp\nZ1LAMmUUvQK8C1jgnPuzc+6HwDTn3NH56ZqIiBQCryVh1OIcLinBKPnOZfqQoFB1IJz0PnElnGu/\n2cQ//mQrrc1FNB+YRuJAfG93DQczN/qsCCspwfM8PM+jqaxi2PQEF40l/YNmDB+vW82QQUhSYlTy\nNIldEQ1Y8umMy07O6/kCUzyWHJ86ULS/rZTevgAQ4ZHv3sYr2zIsR19dQvmPg0z9ehlFRUFqv1lO\n/e8qKD4x1aAt/SArluFBfVt76myU7X0VqWtgS+GYMZWekOe7CHDIZ4JFZHeMKXX+ipi/tLPO1zSv\n0pIwly98iZA38nJmcyraefvcTSO2G2pTuHLkRpKSBXJUdrb/S8aCOYrmx4DoJC1QpGLWUuAyfau0\nAtcDG4E1wM39n/+9c+4vOe6XiIgUCNeZXCsjkOnB9ZAbmTovOXC0pbsoaXDyux/VAR70Dnn6nOa+\n0TnHHzsahi68lrrt0G4NWxUm/UGqPNW+yKfrf/7p0e88ipvnKhdOO0iun9rJI9/9IY9893YAtjVm\nztypWpwcyAlM9ai5oQzX//Gy1R3c8IvNfPayPzOvfvhUHwNaj0p//L1NqQfPMRxt0dxMBZExdP4J\nxMp91OsxIxDxd1EHZ3kEfWRl7GivoLGxCvOxhH0oFOXCuRuzbn/+KAJFz/ZMHbmRpJbjIIKL5ugE\nHrjJHAFRRpEUsEyBorcA/wEsBL4JnAh0mtnfmFl+H/2JiMi48Xp6k+r5BJNmKySPtnf0JQ+a60Pb\nB342g++3zkza3rw7BDiCz20h6S4ozQ2RmbEksjnlIH/oR0mBopCXNEfOhrdI2ndGSDVeCko4feBu\nFDNcWFaSuYBvorOP30TKC9KMkv/dmXIfr8JRfEKQSz7WyDfv3cSqMzq44JRXuf36ezhpWYp5ZrXp\nn6iHw+mnQTZF02eqxJpTr1QlsHzNktHtmE2E+lA5h4V8RHCA0DJ/0462tlVjfR5dm7Jfwj4WgynF\n2ddBmlLkr2YSwI6+Mt/7SJyL+rtmsjWQqRQ1cpHCaB5YbmbNHR4UKJICljZQZGbXm9lZwBbg50AA\nqHPOPeqc+32e+iciIuPMxQzXNhg4CWZYXGdX3+CULefgsurkORTHW/LODXPDgPHQX/6HxGLWafvi\nHLuK5/ger/UtmZ2xDE5pSTjpvA0hfysISW55u9MHdgL+FocCIET2g6pgEDa++4fE50j0X3hmEIkw\n657UgSKAskAf77tuT/J5gzE+/s7HffY2/cX+ejjTQF+BonRu+uNXRrfjaxmKSY2hnSvbfWX7eCWu\nvw5WdmaWxRcdOPB09hk8jz01n/a+7ItNd0T8F6aeHcy+ZlKhu/i680duNJb6/E09zJZVxR/+OCDQ\nMYqo/EicozecNuNuwpc70dQzKWTZTGh9yMyeMrNbgR1mdirwNznul4iIjKOV5xyb9P7gyicAXob7\n0fU9U5LeBxKeFDoHF9XvTXoo+f/u2MJDu57vf1I/WMw6U1Tnd7vriUTiT7h//LX4yk/pc4Pi72Nz\n65I+6xsSKJgxfXDZqbpANxWZfknJu9ArO9NmcwQ7Un6cUbv5XyFo47t/yONn3sqqlheZ++knWPjZ\np4F4lttQke1RGjq7KCkdvm1eQyslRYMZUiPd+8+s35t2W6umnuVV8SuN+ckqotZ3gfeYj8DSETUH\nqK7opntrOfsfzVCDC4h0HvwSL+a/dhyR9Tn+e9fc7DvU74zKPSM3Okx89GsfyOv5rDw3CzDEpgw+\n/Am2hDO0HL3u8CReVFsZRVLARgwUmdnnEt5e2f+ZHlOJiExgX3vwi0nvvf2Do/Gk+tTO0dk1+OR4\nT7SMTKUMioKwP8V0mVAIPn3xg8DI0xVm/fwA589bxltnr+BXN6cejCSOsWLTKiGU/MRy/0qX0Mqo\nqxvMfDqjcrevIq8yNu5s/F7abV5PJO0AvShD4d9INPVtTkem6tEZ1NbC305Zn1Qofc+FHfQ81Ufs\ngGExo+exCPs+1cXOTUX09Q6/kHY3V9LTG78eDWjPOPvHmDenNe3WoasMSvYaFtSN3GgID6AvB1kV\nY6Cj10cdJODYBfEAZMv/1rH39zMJNyV/L8f6HG0vVLPzzjn9n3j8fOMxWc0+au4p5T8b/QeKji0d\nXsNLshTKzfyt6IypA/GJUGNuvm8OHEg75fDVnJywUPgNEilQJHnmq0S+mT2Xq46IiEjh8vYNZhSF\nhizd3LQvsdivY2vv4MjXueTxvXOwIZx6rtA7ztzO/9zyMxzdw56mx2IxzIyuB3uZHeskPhs6O5HF\ns5LeGxCrGvznr6qyi6JQfPAXJMabKoY+1VbUKB9qazNnNgQ2ps42yDQVsrs79fSX7kMoinHTp2ck\nf9AM+6/uZs9b2tl9Wjv7P91FdKdxYF+I3/4w+XeKxeC2+1eReE11Hp/5+gpmGP8fl3FgnfnvOdn9\nfOO/jWq/wI59/nfKQ2xpr6VeHS+d5QsH/3vqWF/NjtsXsONn89nzu1nsvmc2W//tSJoemElkXzGd\nncWAY31rLd944cSMx+2OBPnUY2dRWeM/qDBtNPNIC1mOFiJLKUeZblZTPrBiaGh/bjKKunpykw11\nONDUMylk+fwKExGRw8jq81cO/Ox19+I1x1M3Aj3g9Q7esbS0JqdEPNM9Le0xnYPVJenvdpyDtbfc\nQdKjs/Z2PM9j33faaf3HHh64I/0qVMaQaWjFIWJ1VUltwhXJczRmJqxEdUJZE2XekFFd8Kdpzydj\nLEPMJLhhd8rBUKiDtIOkjs7UA5B9kdENTF54FCBD5GbIpfPDG2dy48/exH8/tYAHHjuKT/7rBTz8\n5JED2w/tvt8oD6Sfb+SNEHiT0Qns8J9UX7bb/3kiPme/Njp/haCPXTg88BreXUrnK1V0bawk1hMA\nDK+hnXUvDAbbb33lOK574gxebx/+PfzXxhlcsfZ8Hm+aRd209mHbMylxEbwJFpO/c0/6LMmx5vak\nzzw8VJFF8eB40c7cZBSF09comvjGOKPIOTfHOfcn59x659xLzrlrUrRZ45w74Jx7tv/1xVTHEpnE\n/2WKiEgmN9x3PWd77xp4H9i8l1htFY74dJ+e/nFoZ1cJsZjD8+J3Ma+FkwcQQ6dxeSM8onAOfrbm\nHt6/9l24jj7mfXE9nF1J7y8Pthj8p2toYGiovoX1w1Y7aznWG9grFIpQO21w7tKZlcNHdF7tSZk7\nLGPm4ejdSddcIq83gtd4gFj9kDpYfVB0AHqnDN+nvaMMGD6A6rYg4ZhHsedvpaDr3n0sfjLMelcf\nyYNPTOXBJ4bXZDVg3wi1hEszrBzlp3ixpHbTI1/h2tP9jZG8fR3Q0wsl2RdrLmmBrjkjt0u07oV5\nrF65NetpsLv7/GUUHT23ieJQhHBf5qHAvhWl0JM8Le3erUdx79YjOWn6LhZUthI1j3XN9Wxoi1/Q\n5WU9A1ma2TqxrNFX+7jCHsaMlCU5loqe3kx4ZvbFyf2Izp5GdFszxdtGURAuCz1h/4XPJ4ocZAlF\ngGvNbJ1zrhJ42jn3sJmtH9Luz2aW54rrcrhRRpGIiKQVCA3+MxHY1QI98SK8oaS6MI7WhBoDG8JV\n7Muw4o1z0DvCDIN/e9s0FnzqcY74wtMEgNafpH6SObSIddJ7B9GFDUntY86whK41TG8ZGIjNC3Ww\nqNjfU3DJr9BL21N+XpImyaO9I/3gudFnVtFHz56LnyBRrLQIm1GTsU3fsZmOZxyz9PW0W5cVq57L\noVp+6hLf+zj6vwt9KGrH99Livb3lvqYT7UhYcTIbwYBx+nHpry+AGI7unnSFhh2PN87il5uW8avN\nSwaCRGAsWrDLV18ALqxO/d92RsHP+N9ngvIiUVxLbgI5AL0rjyDYY4R2j31WUTQaSDtNWPwxs91m\ntq7/53bgZWBW5r1EUlOgSERE0now/KuBn50ZwS3xAqilTSQNYnY3Dj7JNBx/bJ+ZdJyh453QCLWE\nf/LYBhLLTXf/IPPT6VTDqcjsaRBMrkXTupiEoxozpg8O+M5IkU1E8OrMHZUx93Ds7rTbvANdeLv2\nD/s8XaCou6eI7u7UF1tiLa1snPa2HvwEisKnLRmeTtfPgNaqlJuSlGSIZb21akfWfZH0vrP+q773\nCW5t8tXei0Cx35lBZuz/QfbzzzaFq2iP+suwufycZ0k3n8WAA1lco0M1TG+lqmLkRQkSVXq9TA36\nr0/k1X7I9z75lun7bKwFdudu+hmlRYRPWUz5ywdycvi2DEH9Cc3/1LNa59xTCa+r0h3aOTcfWAk8\nkWLzG51zzznn/tM5t2zMfh+ZUBQoEhGRjM754JsGfg5u2I3rChPsgeKE8fr+lgrCCavu/LmznqgN\nDpJTjZd3Z6iL2dvjMWdRhirFQwwd6hgQWT5/2Gfh6YMdmVbTTnFxfCBW5vo4KcXUB6/2k1n3QcZQ\nhnhMaP2OYZHHYBiKWlMNeB1N+1OPdl/syZztM9QlH2miYW52xVz7Zk2FDMtVG9C9MnPQKRDoy7DV\nWFySYbm3Ap+SU0gWL17sex+vtRO331/2RrpgZlrOsaNkJrGe7LKKYriM9eFSWTCzhXNWb0i7faRr\ndKhQKML8uXt97QPwsdpXfO8jwwX25DbL0KrKCBalmOM7Bva3+gvcTwijW/Ws2cxWJbxuTXVo51wF\ncA/wKTMb+o/FOmCema0AvgPcO8a/mUwQChSJiEhGn/3hYFaNi8QIPbcFgPKk2QWO3XsGB97tsSIe\n70xefnro6mf1IYhY6oHIXx6MsX1jEYkhoHTDpRgkZR8BRKdVQlFyLaOmpZGkVjMSilifUtGYol7N\nJH3CWQAejmbIKmrvJvD68KBeeZoEm31pAkXPdU9Ne/2lUlxqXH3jTkaqKBotKyKyalHa7dnUJgLj\nxDdszLA9lrHwr9cwtByFZDKarI/QBn/Tq0qa8b0yVduSOtqfzP4afarLf02cj7/zcSrLhmYAGZnC\nlOksmLeHUNBf3a8ji1tZUjKaLJXMq68Vkjsb81PU2h3ownXlZmWyg0LRErzw2BfWaWmtIObv0jns\nuVG8sjqucyHiQaJfmNm/D91uZm1m1tH/8wNAyDmn1Q9kGAWKRERkRIkDqcCeVgI79lG8HwLdgzeM\nexpriEYHb2V+c2A+fRmyipyD3x9IrvDaF3Ns2+j4+ieXAwEOluxNV7T64NkTt8WA3iG1RwyI1A0G\njkpLwtRM6ezf1zizIkUR64bnUpxR8uVL912XdlvoxW24tuRaGSX7INQ2fADT0VlCV9fw+hc9FuSl\nnuyfjr/wKHzhivkZ20SnVtB71nLSRXGM+MJomWsTwZTqAwQC6bdfU/dS5s6Kb2dcfrKv9t6uFlxr\nZ9btA33JWZjZ2sLsrNuu75nie/pZTWUPH73or8M+j551AD/r8tVMaWd6baYst+ECxPjItNd87XOQ\n1/DzUe03HvJV1NoB3u7cZhU54t+1Yy0aDXCgzV+drQlh7Fc9c8CPgJfN7Jtp2jT0t8M5t5p4PCAH\n/6vK4U6BIhERycrn7vg/Az+Hnt+K6+mjatPg9r5IkM1b6wfet0aL+U3r/KRjDM0quqBiG08mPAUP\neUbtAo9r7tgERAcCRKmG1TFSFLAGwueuxCUM1A1oPi55hNaQkE20pLiVhtCQaW7By1OcUfLp1PNP\npKou9XQEF41R9NeNEBmsXeWAyi0pW7NzT+opOdlmYLxl1lKue/dy4rdNw69GCwXoPW4+vW9aNqwu\nVlI7oPFNIz0XjnHM4vTZKhWulxWjysCQTK7/+adxPu6KHf3TIH0o91/jmbZQFa3rRyjq1i+KxyMd\nDSM3HOKCU17h2IV7+t8ZpUVdtESqyS6Hwaiva2XxkTt9n/fcqh1MC+Y2A6ZQ5KtWkd/6WaMxmus4\nG/tbKnNz4ALmzN8rC6cA7wPOdM492/86zzn3UefcR/vbXAK86Jx7Dvg28F4zn+mOMikoUCQiIlk5\n+7IzWH3+SgBcuI/ix1+jtDFGSVNyVlF3z+Cg5uH2WRyIDr4fmlUUCIALh1nfUw3Alacs5B2zl3Lz\nFQuBwIhZRInD9hjQc8YxWGlRUtv2sgiR6pqBloFAlIbp8aKfIRflPTXDV/7xar+U+Y8heXHP3h+n\n3ea1dw9MgzyouAWKDgy/393bNCXpujzoqa5aumLpAzttLY63zFwOFiRlgIh40fSeN68gekT9sO1D\n2+7JomTognl7Mi6J/g8Nz2VeMj34l5FPIin9V8TfYN7b24rXnH0WTfF+CHb4H49t6pmDZblq2h87\nZviaUnnQte/9M0XBeM22TW8sJZsgUWlJmOVLt3LUwl0EA/7mDdUHu7moepvvfgIQvG90+42zxFVE\nc8U70OXrmhyNUCcUtYx9XKFpf+Wkm3421hlFZvaomTkzW25mx/W/HjCz75vZ9/vb3GJmy8xshZmd\nZGb6R0NSUqBIRESydsN91zNtdrwWkdfSQdGTG6neYLjIYPhm/fo5A5lDhuPre49NyiQamlW0ONDJ\nt946nbfMXM7u18tJl7UxVNJ0s6Ig4XNXwpTyhDXNoJcYdnpHQmvjyAWDg5rLazYztyh5+ojXMLqp\nEJIbmZ7EB7c1E3xpcFltB1S/Ci6afEdt5ti2o46heizIfw9ZoS9RIOioqUuubXVQrLyY3pMX03fC\nIijJnPFhQGMtUJv5ug4G+5jZkH7qyLLi/cwMZV5RysvTNJeJyk/mhwNCT22C3uxWJ3NA5Vb/ferq\nLaGpqTqrtvujJTzSkTlomcqCmS1885r/oOqCZkYaHnguxrzZjbxh+Saqq/wvl76oqI1rp79AMMsU\niWHnr/VfgLwQJK4imkvBl3O/ImLV8Ocrh6yvL8S+llEstXc4G+NAkchYUqBIRER8uWvbrZx8OiiM\n5AAAIABJREFUcbyQaGB3CyV/fZ2qTYN3MF29JWx5ZfrA+52Rcm7bd+RAgMi55GBRRVWML/94KytO\nSb+KUAwbVo/IgFhNOeETFhI+7w0wJJMoQpRZH3yezu4pA3vNbNhP3bR2PIx3TXmdN1XsIUnwcz7+\nEpIvmQbvodd2JQWLQt1QnWIhp8bmarq6h9cqerBtVtqsovLKGB/5x8F5FhbwiNZW0XvcfMJnLSdW\nP/Lg3YDdSyA6YjaRsXrlBlyadKEAMT5e93LmQxymmRaFxk+wyOvupWjd5qzblzRDqN3/iG/zjgZ6\nezMUrkpwf9vcpPpw2Vq+YC+/Ouv3/O1RLxBwqVM7aqe28YYVm5g7uxnP5yii3OvjA1M38PmG56gb\n7ZSzw/wav+mRr+T8HIHmdrw9rTk9R1E7SdnEY2X7Tn8r9x3WfE47G2VcVWTUchoocs6d65x71Tm3\n0Tn39xnaXeycM+fcqlz2R0RExsaX775uoGZRcEsTU377GmU7BgcWOw5MY+/mwVXQ/tLVwD2t8wbe\nDw0WzZzfy9fu3swP/vga53+gmXjJXxt4iObhBgNEAY/IvDrCa5YRXnMMsdm1SXPa4plEYcqueI3n\nNx4HOJwzjpi7l4Xz9zIt0MPnpj/PeVVDn7oeg1f7oTH460gujBgsemEwVaNsL5TuGXpX7Xh146xh\nUxu6LMSDbekLBp/xjlZWfKCEnjcto+f84+k9bUl8mllg5FuoGLDnZIPp8fNnanncMZszFrC+rGYT\npcNW5kt2uGZaFCI/waLA7hYCG4cXxE/FAZWjyMbo6wvy6sZZWS2c1hItZm3HDP8nAcpDEa4/7jF+\n++Z/Z/nUvZSWhKmva+HIBTs5+YSXWXLUDkpL/K6JZryxrJEbZzzNmqHBeZ8O92t8+alLWLBy3sgN\nD1Fo/faRGx2iytfxvZLfSDq7SmlqnkRZRcookgKWs0CRcy4AfBd4K7AUuNQ5tzRFu0rgGuCJXPVF\nRETG3tmXnTEwmAruaWX67S9R3HiwuLDjtcYG9jcNFiP+j/a5/G7/3IH3qRInbrtxKvf/dCoHp58l\nFrKOVZTQe+xces5dSd8bFmA1wwsdG0Zwahvta2Dr9vjKZ9VVnSxf8jrnHLGRq2vX8y8zn+TokqE1\nHMrxGoatIisF5uHY3aw859iU20Ib91D0v69AT3wQW71h+CpoHZ2lbNk+fdi+D7fPpCUyPNvooE9f\n9xJT5nlkn0Jh9NQYe04HC2WeSllc1MvxKzZRWZE+w2Jh0QHOGGmArdpEY85PsCj04na8PdmtOFXS\nAmW7/I/6Wtsq2L4ru6mF97bOpTlS7PscBy2t2cdvzrqX75z5ICsWbadh+gECAf99nhns5HPTX+Cq\n2lepCvgNMA0xQa7xHzz9jezXOh8l70AXge3NOT1HqBvKsouP+rJl+/RJU6tIGUVSyHKZUbQa2Ghm\nm82sF7gLeHuKdl8F/gXIPOleREQK0sOxu7n4uvMJ7O+k4dvPUdTY27/F8fLm2TTvG1zJ5N6Oefxk\n3yKiKW54IhE4651dLF01uAKZOYjOqCF88mLCZ68gumgGFA1f/tkwosTYfUqM7cdWEfWCzKxv4fTj\nXuHaE//CLUc/ymfrX+T4sn0Eht2gz8NreObQ/xCSF1978Ivc2fi9lNsCjQco+eMLeI0H8GIw7fnh\nwaKdu6exvyU5yNhjQX6w72jS1QueMa2Dm6+5n1l1I682Fg0Yjcc79i93qaOh/ZwzZs1oZtVxGykr\nTT+APqq4lX+ofz5zAWuqVJsoRx6O3Z1VBogzo+iJDXh7s5vyU71pdIWtt26vo6V15GXEuyzED5oX\np/yuzZbnYE3FHv5tzmNcW/cCb6ncwYKiNgKkH8WXuAjLSlq4qHoLX25Yxw0z17FkTFbpWzKhrvGH\no7lfBS304ras62eNVtVm8MJjG8HoCRext6lm5IYTgTKKpIC5XK2G55y7BDjXzD7U//59wIlmdnVC\nmzcAnzezi51za4HrzOypFMe6CrgKoL6+/vi77rorJ30Wkcmjo6ODiorUS2/L6G1Y9zqGEaktIVYy\nOI+mqLiPotDgUualLkJdsAcvzSOycI9Ha2sxbeEyYiM80zAHkTLAA88zQqEI5aFeqgJ9lHsRXMbH\ncNMgOLopGjL+tr+6i56O4c+ZDKA4iJUUYZ4jWgqWOK3LGWWlvXhe8rUxxQszJdhLOhZzNLeV0dJW\nivXXgDEXP7YFIBYkq0dwXiBGcVFfxgwND6MmEKbS6xs5+yB4zMgnlUMSiUR4/bmRV+kywCpKIDhy\nPSHzIDJyzGc4B6UlvQSyWGms2uulZoyXoDdzhM0jbAHCsQDOGcUuSomLUeSiucmWGedrPFf3DBue\nzr6+1WhYURArG31mWTZiQYiWju0xnTOufvelT5vZhC1LUjZ9ji2++DO+9nnm+5+Z0H8TKSzDH8vm\niXPOA74JXDlSWzO7FbgVYNWqVbZmzZqc9k1EJr61a9ei75Kxd/Bves37/5mHa3roWTiYTdQwvYUF\n8/YMDI6nBnp439RNHFe6P+3xOrpDPPjEUfzukaVs3Zv8hLG3wmg9yhGtilFb3MG86U28uX47ayp2\ns7C4feTOBv8yoZ5QT0pr4v/vw8s/w5YXh9fkMM8RWVBP75KZtC4P0VM3OIINhSIsX7KFsrLBwJDD\n+Oz0F0bMgNjcWMNX/ngqT3TMoK+CeOpFFoLBCPPnNNEwvSVDhpBxUlkT763ZTHUWU3W0Sl8evRm+\n/uFb+K8f/U/GZuY5+t6wgOickb9feqbB/mVkzD5LJeBFWbZkG9WV3RnbOYyra1/mDWX7fB2/kBTC\nNZ6re4Y1a9ZwtveuMT9uot7jjiB6xPApt2OpfS60H5Hj+XQTjbKEpMDlMqPojcA/mtlb+t//A4CZ\n/VP/+2pgE3BwmZsGYD9wYaqsooNWrVplTz2VdrOISFYUKMqPBd++KV7ssn8QVFLcy6IjdlMz5eCS\n9Maq0maumLppxEHxuldn8sATR7HBKnm2pJZoibFs9m6OadjL8ZXNnF6+h4pANmn28/AaHj60X0wK\n0n0/eJDvfOxHwz63gEdkTi1ta2bQurxkILCTKlhU7vXx2ekvMK+oc9hxhrp789H8y/Mn0dpbknJ7\nIBClqqKbqsouqiq7mFKdeSnx6cFu3lezkWNKs1yxKPg9vNqzsmsrY+rzF97IX+9PP2XVgOiCevqO\nmTti4fOu6dC6mFEFi5YevX3E66rIRflU3UtjNAUszwokoJ/re4ZcBovMc4RPW4pNzV0WtQEtS0kK\nxh+q16+5bkJnz5TVzbHF7/SZUXSrMookf3IZKAoCrwFnATuBJ4HLzOylNO3XkmbqWSIFikRkLChQ\nlD8fvPce/rRtS0LAyKib1sbC+XsI9U9HK3N9nFO1i9PL91CTYepPoj5zhLKo7pgQpyqIJ9OSH88/\n+jLXnv7FYZ93Laym6fKFRKbGi1cHgxEWL9qZELz0Fyw60FfEXTuO4r6ti9jdXUFxUWQgMFReFs5i\n7G/MDHVxevlezqzcldU1He/41/FqU5V+lHx65ZVX+MTS/5t2e6y6jN7VR8ano2Uw2mCRczEWzNvL\nzIbMhbQPy2BRgQSJID/3DO+a8UFa9w5daGFsWEkRPWccAyWhnBwfIBaA5pUQKR+bYNFEDxSV182x\nxe/wFyha90MFiiR/chYoAnDOnQf8KxAAbjezG5xzXwGeMrP7hrRdiwJFIpInChTl34Jv/gsD6387\nh+fFmF57gBn1+6koj9fQCBBjZek+zqzcfcgDmqjRX7i6DK/h2UPrvByW0j2lb54DBz69Oj4o7w9e\nzp65j/lzGgfG6eVeH9fWvcgRxR0pj5HyuJFiNoSr+l/V7Owrw4YUbAkQY15RB0cVt3FkcRtHFh+g\nMqtMuAQKEhWkdNebBT0iR84ksrABQulrF3XXQstiSFFxf0TTato4auEugsH0dYuKXJRP1K7PPmNt\nPBVQkAjyd8/wq2/cy22f+0VOjh2bUkb41KUZr8FDFS2C5uMgWnrowaJJESi6yGeg6DYFiiR/choo\nygUFikRkLChQlH8HB1GbvroSKvqXIu8fpFdWdNMwvZXqqk5KS+JT0Mq9PhYWtbOouI1FxW3MK+qg\nzIumPHbMoDVaREcsSF2wh1IPCP4Er/akPPxmUqhGms7RtCxE2wdXDgSMqiq6OGrRzoFrsMhFubxm\nE6dX7B3V+btiATaGq2iKlOBhzAh1saCogyLvENZ+Dt6HV7t49PtLzox0vXXPngbHzoXiUNrMob5y\n2L8UomX+B9rFxb0ctWBXxqloAWK8t2Yzb67MwbrmY6QQMz/zfc+Qq6lo0WkV9L5xcU6DRZFi2Ld8\ndNdwoskQKFry9k/72ufpH107of8mUljGrZi1iIhMLqvPX8lf73+Ghf83Xtej6YRa2t4zH7wA7e2l\ntLeXgnOEQhEqK7qpquhia8lUHglGCQZjBAIRQhjlXh/VgV4qg32EAhFKvDBvCwY46pj7KJznz1II\n7mz8HpdO/1ja7XUv9VF7/TPsPPEowhdU0NZeytPPLaS+rpW5s5qhGH68/yhe7pnC+6dupDRNoDKd\nMi/K8tLMU4L8KMQBtAy64ksXc8eX70m7vXTHPtixj+7TFsPUyoSMtkGhTqh7Gg4caXQ3+Btoh8NF\nvPDyPKZUdTJvThNVKQpdR/H4RcsiXumZwt9Oey1t8H18eHgNr4x3JwrCw7G7efT+J/jyhd8Y0+MG\n9nVQ/Of1hE8+GkqKxvTYBwXDUPsstCwzeqtV4FrkcKWMIhGZlJRRND6GPiWNVZUSPnUJFIdoBnpX\nAOXEl1d2xP9PIL7i+PTSUu57x7upra0l1ngtxF6E4M3KrpCMzq+8nHBn5rpXBkQW1BNZPo9m5+g9\nGbyiCPPmNjN9WhtFRVHqg91cWrOJFWMY+MneEryG343DecUvP5kg3QAXHJ80JTdpex20HTHaaTxG\nzZQOZs/YlzbDaFqghw9Pe5WjS3JTF8eXwCV4dTeOdy/SGs97hu9/7qfc8437x/SYVhIifOKR2NTK\nkRuP9hwO2hZC56zRBYsmfEZR7Sgyim5XRpHkjwJFIjIpKVA0PlI9IbXiIJH59UTn1mLlxQODpY9e\ncSpXvGP41LHYnvcBTwy8V5aFjORtFZfR25V5Vb0v3Xcdp55/4rDPX2l+hX/a/PWB9wuL2njHlK0s\nK8lHnZcQXkPKNUCkgI1m2lA34L3zRGJDbstjDroaoGMexIpHN+AuKw0zvbaVaVPbKSsdGjQ1ji1p\n4Z1TtjK/KPt6XGMngNfw8jic159CuGe48X3f4k+/+MuYHc88R9+x84guqB+zY6bSVQ8HjgTzWXtr\nMgSKll7oL1D01I8VKJL8UaBIRCalQrjpm8xSDaSq33kKzV6Ud523ko9feWbStubmZr63+1aunnIH\n1aEUhX8LrPCpFJ7m5uaU09Dq5k7jl1u+P/D+sk/exv6WLv7p+vNYuWRR0v4n//ImTljViOfByRV7\nOaW8kaNLDmS/UlnWgngN68f4mJJPfoNFD8fuBuDUi1NPNWquhd4l9GdbjnY6j1FWGqZ2ajuVFd2U\nloYpKe7rP5yxsnQfb6vawcLi9lEe35/DKchfSPcM7517Fft2jF1mY2TWVPqOmw9FuVsRLVIKrUfj\nayrapAgUXeAzUPQTBYokf1SjSERE8u7goAjgoT+9wFdveYhui0AU7vz9Ou78/Toevec6AD7w1w/2\ntzRaqoupJkWgKHIycPgMOiT/amtrk667oYYO0D/xhXsBuPKS1dzY9OTBo/DYU/GA5P+ylHsXbOe6\neY+xZmoLnhdvMeoxPGg1swliNBlFZ3vvovsdwzPaDqptBv4c/3nXyTZ4B+/rgnN0dZewbWfJ4Ccu\nSnVVJzPqW3hmquOZ7lreXLad06oamRHqzkEQtAav4YmRm0laH/znS/naFf82ZscL7txPJBjDVh4V\n/+CQvsTSnKMbpj0L3dON9vljsyraRDDm/3mJjCEFikREZFx99ZaHUn5+6sXfYOHfDU5JKPcizC/q\nzFe3ZAL6/IU38tf7n0n67M7G73HRR36Sdp/vPPFXWDB8UOOcUV3Vwx3dx3DHzvhni2jmmvqXKQ0O\n1inOPOaqgeBPVWdLfJmZMPto1xv7g0aen4G39b/6OPXETcO2/qFrDn/omkMtzfzTzJcJBA41dlCM\n1/DCoRxAEnzt/WMXJDqoZGsrbP0r3ee/AYLBnASLHFDWCKVN0DnT6JgLsaJJHjBSoEgKmAJFIiJy\nWIiYR8x8jodE+qXL8nj3EZ+Cs1ekHBjFAvHaMKlMq2mntCS57tFGavnE3tMoc318ecYz1AbD6TsU\n/Ble7fAaXHJ4Oyc4umXNzTkw8z1An7IJWpf42+fYJVvTFrhO1EwtH951Gh+Z9gonlTf5OkciBYnG\nWCx3hy69fx0A3RccHw8Y5YAzqNgJZXviAaOuGZM0w8iUUSSFzRvvDoiIyOTT3NzM2d674oP3odVb\n0whbgKe709Uh0j9nkt7ZGQbvsTm1aQfn3XXpC7BWV6XPbntz5a7MQSKAyPszb5fDko1yEG8loVFl\ncfRV+D9XZ1fJyI1kUiv9/dM8es91BH0WoPbDi0Lldqj7K1S/bAS6LB4snUzM50skj3RnLSIiefX8\noy8nFRX29iavHhXzoHUR7DoVNm2ZnrTtJ/sW8Xx3zbBjeg2v5KazMjFkGLxbRfpBczTDeDrTmP6E\nsuYsOiWSYJTjcRvFnfyepim+2u+LFPs/iUwIa399bc7P4QE9dRAtG3Gu7oTiiGcU+XmJ5JMCRSIi\nklfXnv7FpPeh57fiuuLZFwa0LIWuWQ4Cjt17p3KgrWygbZeF+FbTMdzRPD/+QfDyw2rlHMm/5uYR\ngjZ90bSbvL60m2hrL027rSKQYUeRFFw4MqpsikzXaDpdXSW0HigbuWG/x7umj9woneCXRr+vTBqB\n3vHuwTgx8/cSySMFikREZFx5XWGK//sFgs++TrgGwtMGnyiaebzw8lw2bJ5BV3cRAGWU8v5j/guv\n4TW8Wg1CJLORAkWBXfvTbittJu3NefO+KsK9qWt4NPalDyKJpOKiMejyP1ouHuUq6Rs2z8x63Lmj\nr5zdo7ymvdrLR7WfZJDnpJvZDVU5P0egO+enKEjKKJJCpkCRiIiMOxeJEnq9kf3Lh98Bm3nsaazh\n6ecW8dPVP+J7q28Zhx7K4Wrx4swrinlNbXiNB1JuC4ShfGfq/WLmsf7V2SkH24921vvtpkwQF3z8\nnNHvHPB/Wx5qI+s6b4l6wkW+FgaocaMZyZ84in1kJJ/4tw/m9Xx3ffeqnJ/jm5eckfNzFBy/9YkU\nKJI8U6BIREQKRk2xamFIfjkg9MQGiKYuZFQ2fPXwAR2dZdQEq4d9/tfOdEXXB2nK5MT0ye98eNT7\n3naT/309GPVSkD9Z/SOWVx47Yrufrv4RZbP9Xq9leA0/H1W/JLMLP3Juzs9xzgfflPNzJDrrtOPz\ner5C4WL+XiL5pECRiIjk1cOxu1N+fvF15/P0R65Ou9/mT+a+qKZMTF+677qM271IlEfv/dyw8fZ3\n/t9FPHHPdWz+5LWsqE2u0/LAZR9g8yev5ebjv8lPV/8o6XXr6p9CMP21TPAvo/1V5DCw+vyVvvc5\n4/KTWby4lmOObvC1342fPX9U340H97l2yaf46eof8YUF1xMgMLB9UenCgev5oHhwMzD0UCkswWt4\n1nefpHB89ofJ31+33XBlzs71xjfMB+DWc96Ws3MULGUUSQFzdpgVxlq1apU99dRT490NETnMrV27\nljVr1ox3Nya19x/9cXZvaKSoNMR/dP5y4PPm5mZW//KnSW0fuOwDLK4dOUtDJJ1H73+CL1/4jZTb\n0gUvx0Ks+TaIfAPwIHg7Xu1JOTuXFI73Lfo/7NnclFXbOYtncvv6mwfef/lb9/HwoyNn8Nx2w5Us\nXjz4vbjg2zdldb6xCLrH9pwKNCZ/GLx8wtaNK8R7hrO9d+XkuNNm13DXtluHfX7me79Fb4bi/6P1\n6D2Dgfyjvn0Tkf6fX7/muqfNbNWYn7BAVEydYyvO+pSvff7ym8x/E+fcHOBnQD3x0NKtZnbzkDYO\nuBk4D+gCrjSzdT67L5OAAkUiMikV4k2fiOTHr75xL0tOOprlpy4Z767IBParb9zLbZ/7RcY2n/je\nB9NOJfrsDb/hsXVbhn3+mQ+dwTvfmnqqzqcf+D2/25g6yHTN8au55pTTMndaUirEe4aDD1vGWqbA\n+akXpw62j1ZikOiggwHPCR8oqpljK866xtc+f7nnsyMFimYAM8xsnXOuEngauMjM1ie0OQ/4BPFA\n0YnAzWamgmIyTOrlOkREREQmqPdcd9F4d0EmgfdcdxHvue4impubuWLWx4n2xYuMBEIB7th5C7Uj\nZEl+/fOX+D7nt867gG+NqrdyuPnZq98d86yiOxu/l3H7o/dcN2bBolRBIohnvGWbHXe4G+uVzMxs\nN7C7/+d259zLwCxgfUKztwM/s3i2yOPOuSnOuRn9+4oMUI0iEREREZEcqa2t5cHwr3g4djcPx+7m\nwfBdIwaJRLIxltNm72z8XlbX5aP3XDeaBfqGHSMT1SRMq9Y591TCK+2SdM65+cBK4Ikhm2YB2xPe\n7+j/TCSJAkUiIiIiIiKHoYdjd1NeXXrIx/ATvPyfu6/jxs+e7/s8552xZMQg0aTiv5h1s5mtSngN\nLyYFOOcqgHuAT5lZW65/DZmYNPVMRERERETkMHVvy89obm7m0ukf87Xf0lOO4uY/3zCqc55+0mIe\nvWcxAGvefRORaPp5VPf+4Epl0Q3hGPupZwDOuRDxINEvzOzfUzTZCcxJeD+7/zORJAoUiYiIiIiI\nHMZqa2sHpqJdUHUFPR3htG1veuQrY1rMf+2vNVXMN7P4awz1r2j2I+BlM/tmmmb3AVc75+4iXsz6\ngOoTSSoKFImIiIiIiEwQv2+7Y7y7IFnIQUbRKcD7gBecc8/2f3Y9MBfAzL4PPEB8xbONQBfwN2Pe\nC5kQchoocs6dC9wMBIDbzOyfh2z/KPBxIAp0AFclLt8nIiIiIiIiMuGM/apnjxKf1ZapjREff4tk\nlLNi1s65APBd4K3AUuBS59zSIc1+aWbHmtlxwNeAdClyIiIiIiIiIhOCM38vkXzK5apnq4GNZrbZ\nzHqBu4C3JzYYUoW9nDGPq4qIiIiIiIgUEANi5u8lkke5nHo2C9ie8H4H8YJZSZxzHwc+AxQBZ6Y6\nkHPuKuAqgPr6etauXTvWfRWRSaajo0PfJSIiIjIi3TNITij2IwVs3ItZm9l3ge865y4DvgB8IEWb\nW4FbAVatWmVr1qzJax9FZOJZu3Yt+i4RERGRkeieQXJB08mkkOVy6tlOYE7C+9n9n6VzF3BRDvsj\nIiIiIiIiMv7M/L1E8iiXgaIngSOdc0c454qA9wL3JTZwzh2Z8PZtwIYc9kdERERERERk3KmYtRSy\nnE09M7OIc+5q4CEgANxuZi85574CPGVm9wFXO+feDPQBLaSYdiYiIiIiIiIyYRiqUSQFLac1iszs\nAeCBIZ99MeHna3J5fhEREREREZFC4gCn6WRSwMa9mLWIiIiIiIjIpBIb7w6IpKdAkYiIiIiIiEge\nKaNICpkCRSIiIiIiIiL5ohpFUuByueqZiIiIiIiIiIgcRpRRJCIiIiIiIpI3Bpp6JgVMgSIRERER\nERGRPHKKE0kBU6BIREREREREJJ+UUSQFTIEiERERERERkXwxcLHx7oRIegoUiYiIiIiIiOSTMoqk\ngClQJCIiIiIiIpJPihNJAVOgSERERERERCSPnDKKpIApUCQiIiIiIiKSTwoUSQFToEhEREREREQk\nXwxQMWspYAoUiYiIiIiIiOSJwzT1TAqaAkUiIiIiIiIi+aRAkRQwBYpERERERERE8kmBIilgChSJ\niIiIiIiI5ItqFEmB88a7AyIiIiIiIiKTiTPz9RrxeM7d7pxrdM69mGb7GufcAefcs/2vL475LyUT\nhjKKRERERERERPJp7Kee/QS4BfhZhjZ/NrPzx/rEMvEoUCQiIiIiIiKSNzbmgSIze8Q5N39MDyqT\nlqaeiYiIiIiIiBS2WufcUwmvq0ZxjDc6555zzv2nc27ZmPdQJoycZhQ5584FbgYCwG1m9s9Dtn8G\n+BAQAZqAvzWzrbnsk4iIiIiIiMi4MUaTUdRsZqsO4azrgHlm1uGcOw+4FzjyEI4nE1jOMoqccwHg\nu8BbgaXApc65pUOaPQOsMrPlwG+Ar+WqPyIiIiIiIiIFIebzdYjMrM3MOvp/fgAIOedqD/3IMhHl\ncurZamCjmW02s17gLuDtiQ3M7E9m1tX/9nFgdg77IyIiIiIiIjLuxnrVsxHP51yDc871/7yaeCxg\n3yEfWCakXE49mwVsT3i/AzgxQ/sPAv+ZakP//MurAOrr61m7du0YdVFEJquOjg59l4iIiMiIdM8g\nOTHGxaydc3cCa4jXMtoBfAkIxU9l3wcuAT7mnIsA3cB7zcZ+6TWZGApi1TPn3BXAKuBNqbab2a3A\nrQCrVq2yNWvW5K9zIjIhrV27Fn2XiIiIyEh0zyBjzoDYmK96dukI228BbhnTk8qElctA0U5gTsL7\n2f2fJXHOvRn4PPAmMwvnsD8iIiIiIiIi48zGPKNIZCzlskbRk8CRzrkjnHNFwHuB+xIbOOdWAj8A\nLjSzxhz2RURERERERKQwmPl7ieRRzjKKzCzinLsaeAgIALeb2UvOua8AT5nZfcDXgQrg7v66WtvM\n7MJc9UlERERERERk3Cn4IwUspzWK+pfde2DIZ19M+PnNuTy/iIiIiIiISEHJQY0ikbFUEMWsRURE\nRERERCYHA4uNdydE0lKgSERERERERCSfNPVMCpgCRSIiIiIiIiL5oqlnUuAUKBIRERERERHJJ2UU\nSQFToEhEREREREQknxQokgKmQJGIiIiIiIhI3pgCRVLQFCgSERERERERyRcDYlr1TAqMi568AAAJ\nHElEQVSXN94dEBERERERERGRwqCMIhEREREREZF80tQzKWAKFImIiIiIiIjkkwJFUsAUKBIRERER\nERHJG4OYAkVSuBQoEhEREREREckXAzMVs5bCpUCRiIiIiIiISD4po0gKmAJFIiIiIiIiIvmkGkVS\nwBQoEhEREREREckXM4hp6pkULgWKRERERERERPJJGUVSwBQoEhEREREREckjU0aRFDAFikRERERE\nRETyxpRRJAVNgSIRERERERGRfDG06pkUNAWKRERERERERPLJNPVMCpcCRSIiIiIiIiJ5YoApo0gK\nmJfLgzvnznXOveqc2+ic+/sU2093zq1zzkWcc5fksi8iIiIiIiIi484snlHk5zUC59ztzrlG59yL\nabY759y3+8fmzzvn3jDmv5dMGDkLFDnnAsB3gbcCS4FLnXNLhzTbBlwJ/DJX/RAREREREREpJBYz\nX68s/AQ4N8P2twJH9r+uAr53yL+ETFi5zChaDWw0s81m1gvcBbw9sYGZbTGz5wFN0BQREREREZHJ\nYYwziszsEWB/hiZvB35mcY8DU5xzM8bot5EJJpc1imYB2xPe7wBOHM2BnHNXEY96AvQ45146xL5N\nBNXAgfHuxBg5HH6XQurjePYln+fO9blqgeYcHl8klUL6Lpks9DefWH+Dw+V3KaR+jldfdM8gh2Le\neHcgl9ppeegP9ptan7uVOOeeSnh/q5nd6mP/VOPzWcBun/2QSeCwKGbd/x/ArQDOuVvN7KoRdpnw\nJtLf4XD4XQqpj+PZl3yeO9fncs49ZWarcnV8kVQK6btkstDffGL9DQ6X36WQ+jlefdE9g0h6ZpZp\nipjIuMvl1LOdwJyE97P7PztUvx+DY0wEE+nvcDj8LoXUx/HsSz7PXUh/c5Gxous6//Q3n1h/g8Pl\ndymkfo5XX3TPIFJYcjU+lwnImeVmWT7nXBB4DTiL+AX4JHCZmQ2bNuac+wlwv5n9JiedEREZQk8H\nRUREJBu6Z5DDhXNuPvFx9TEptr0NuBo4j3hJmG+b2eq8dlAOGzmbemZmEefc1cBDQAC43cxecs59\nBXjKzO5zzp0A/BaoAS5wzn3ZzJblqk8iIgn8zOkWERGRyUv3DFLwnHN3AmuAWufcDuBLQAjAzL4P\nPEA8SLQR6AL+Znx6KoeDnGUUiYiIiIiIiIjI4SWXNYpEREREREREROQwokCRiIiIiIiIiIgAChSJ\niIiIiIiIiEg/BYpERERERERERARQoEhEBADn3Brn3J+dc993zq0Z7/6IiIhIYXLOec65G5xz33HO\nfWC8+yMiMtYUKBKRCcs5d7tzrtE59+KQz891zr3qnNvonPv7/o8N6ABKgB357quIiIiMH5/3DG8H\nZgN96J5BRCYgZ2bj3QcRkZxwzp1OPPjzMzM7pv+zAPAacDbxm7sngUuBV8ws5pyrB75pZpePU7dF\nREQkz3zeM1wItJjZD5xzvzGzS8ap2yIiOaGMIhGZsMzsEWD/kI9XAxvNbLOZ9QJ3AW83s1j/9hag\nOI/dFBERkXHm556BeNCopb9NNH+9FBHJj+B4d0BEJM9mAdsT3u8ATnTOvRN4CzAFuGU8OiYiIiIF\nJeU9A3Az8B3n3GnAI+PRMRGRXFKgSEQEMLN/B/59vPshIiIihc3MuoAPjnc/RERyRVPPRGSy2QnM\nSXg/u/8zERERkf/fzr2FWFWGYRz/P9lZSgiKiCLLtCE6DJIdCSeICoswSoogbzopVGB0000XIVRI\nF0FUF10YBIIUhSgpdJAkFC3xkEoGChFElEWU5iHn7WIvdTfMnmliZI/j/wcb9nz7/da8a18tnv2u\n1c5rBkknJYMiSSebDcDUJJclOR14GFjW5Z4kSdLY4zWDpJOSQZGkcSvJEmAtcGWSH5I8VlV/A08D\nq4AdwNKq2tbNPiVJUnd5zSBJx6Squt2DJEmSJEmSxgAniiRJkiRJkgQYFEmSJEmSJKlhUCRJkiRJ\nkiTAoEiSJEmSJEkNgyJJkiRJkiQBBkWSJEmSJElqGBRJkjSMJC8nuT3J7CQvdLufkUgyOckj3e5D\nkiRJJwaDIkmShncjsA6YCXwx2gdPcupoH7PNZGBEQdFx7keSJEljmEGRJEkdJFmUZAswA1gLPA68\nleTFQWoXJ3k7yVdJdia5t1mfnGRNko3N65Zmva9ZXwZsb9Y+SvJ1km1Jnmw79p9NL9uSfJLkhiSr\nk+xKcl9TM6Gp2ZBkS5Knmu2vALcl2ZRkQae6gf0kmZhkRZLNSb5J8tDx+p4lSZI0dqSqut2DJElj\nVpIZwFzgOWB1Vd3aoW4xcCEwC5gCfA5cQetHmf6q2p9kKrCkqq5P0gesAK6uqt3NMc6rql+TnAVs\nAGZW1Z4kBcyqqo+TfAhMBO4BrgLerareJli6oKoWJjkD+BKYA1wKPF9VR4KroeqO9pPkAeDuqnqi\n2Tepqn4fre9VkiRJY5Oj5ZIkDW06sBnoAXYMU7u0qvqB75LsavbsBt5I0gscBqa11a8/EhI1nk1y\nf/P+EmAqsAc4CKxs1rcCB6rqUJKttG4tA7gTuDbJg83fk5r9Bwf0OFRdez9bgdeSvAosr6o1w5y7\nJEmSxgGDIkmSBtEEO4uBi4FfgLNby9kE3FxVfw2ybeCYbgELgJ+A62hNF+1v+3xv2//rA+5ojr0v\nyWrgzObjQ3VsBLgfOABQVf1tzxMK8ExVrRpwHn0DT22IuqP9VNXOJNNpTUgtTPJpVb00yDlLkiRp\nHPEZRZIkDaKqNlVVL7CT1i1enwF3VVVvh5AIYE6SU5JMAS4HvqU1sfNjM2n0KDChw95JwG9NSNQD\n3DTCllcB85OcBpBkWpKJwB/AOf+h7l+SXATsq6r3gEW0JqskSZI0zjlRJElSB0nOpxXe9Cfpqart\nw2z5HlgPnAvMa55L9CbwQZK5tG4f29th70pgXpIdtAKmdSNs9x1at6FtTBLgZ2A2sAU4nGQzrQmp\n1zvUDXQNsChJP3AImD/CfiRJknQC8mHWkiSNguZh1sur6v1u9yJJkiT9X956JkmSJEmSJMCJIkmS\nJEmSJDWcKJIkSZIkSRJgUCRJkiRJkqSGQZEkSZIkSZIAgyJJkiRJkiQ1DIokSZIkSZIEwD80k8TX\naQfE/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e590f1e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(20,5.0))\n",
    "\n",
    "for i in range(acc_test_all.shape[2]):\n",
    "    acc = acc_test_all[:,:,i].reshape(len(depth)*len(width),order='F')\n",
    "    if i==0:\n",
    "        plt.scatter(fig_params_1d, acc, s=(np.array(fig_width)**1.8)/100, c=fig_depth, edgecolors='k') \n",
    "        plt.scatter(fig_params_1d, sr*acc, marker=\"_\", s=300, c='k', edgecolors='r') \n",
    "    else:\n",
    "        plt.scatter(fig_params_1d, acc, s=(np.array(fig_width)**1.8)/100, c=fig_depth, facecolors='None', linewidth=np.array(dim[i])/300.0) \n",
    "\n",
    "        \n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_ylim(0.1, 1.0)\n",
    "ax.set_xlim(0.3E5, 1.5E6)\n",
    "\n",
    "plt.xlabel('# parameters')\n",
    "plt.ylabel('# accuracy')\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/1000, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, loc='best', bbox_to_anchor=(1.25, 1),\n",
    "             frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\")\n",
    "\n",
    " \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Accuracy of Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e562b6d50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K1oJLFQ0VZ639zFp7mrW2rbX2qeJzjxUnGbDWrrPW9rLWnmWt7WytnevPeEREREREROR/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\nSZIkSVJjDBokSZIkSVJj/j/p7mE18mQkbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e590f1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "\n",
    "plt.scatter(fig_params_1d, acc_solved_all_1d, s=(np.array(fig_width)**2.0)/100, c=fig_depth, edgecolors='k') \n",
    "\n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_ylim(0.0, 1.0)\n",
    "ax.set_xlim(0.3E5, 1.5E6)\n",
    "\n",
    "\n",
    "plt.xlabel('# parameters')\n",
    "plt.ylabel('# accuracy')\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/100, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, \n",
    "            frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/chunyuan.li/tensorflow/local/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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mTZvGypUradeuHdnZ2bRp04aPPvqIOXPm0LVr13zjpaen53vX2NhYNm3aVGDO5OTkfO3+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ed4109b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(20,5))\n",
    "fig, ax = subplots(figsize=(20,5) )\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plt.scatter(fig_params_1d, dim_solved_all_1d, s=(np.array(fig_width)**2.0)/100, c=fig_depth, edgecolors='k') \n",
    "\n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "ax.grid(True)\n",
    "ax.set_ylim(0, 17000)\n",
    "\n",
    "plt.xlabel('Number of parameters $D$')\n",
    "plt.ylabel('Intrinsic dimension $d_{int}$')\n",
    "ax.set_xlim(1.4E5, 3.0E6)\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/100, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, \n",
    "            frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\")\n",
    "\n",
    "fig.savefig(\"figs/fnn_cifar_dim_global.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LFi3i/fffbzPNzPj3v/9doZqJpJuyKZJMyqZIMimbItJZsc7gMrOjgf8FtgX2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\nbwauCR/ZXn8HcEdeFRSpUe6Omb3s7vu2U0bZFCkxZVMknpRNkXhSNkV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4AAAg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\ns41xshnnf3dlUzpb0j8329PGci6z6hcWc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/y7veI7v6oBvjcH1NdXrO/NoTr3sZNvnXJe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LnXwyXH6593zcODjvPO/x7rtw+umFZ5Jd/rLL4JRTYOVKuPDCwuNnl//Rj+Doo+Gxx+Af/qHw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\nIiKJ9v8BPrdK5uCd134AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e551fc910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,16))\n",
    "fig.subplots_adjust(hspace=0.3)\n",
    "\n",
    "\n",
    "for i in range(nw):\n",
    "    for j in range(nd):\n",
    "        id = i*nd+j+1\n",
    "        ax = plt.subplot(nw, nd, id)\n",
    "\n",
    "        plt.scatter(dim, acc_test_all[i,j,:], edgecolor=\"k\", facecolor=\"w\" )\n",
    "        ax.plot(dim, acc_test_all[i,j,0]*np.ones(nn)*sr,'r-.', label=\"Testing: baseline\")\n",
    "        ax.set_xlabel('Intrinsic Dim')\n",
    "        ax.set_ylabel('Accuracy')\n",
    "        ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "        plt.grid()\n",
    "        ax.set_ylim([-0.1,1.1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract final stats from resman's diary file\"\"\"\n",
    "def extract_num(lines0, is_reg=False):\n",
    "\n",
    "    if is_reg:\n",
    "        valid_loss_str     = lines0[-5]\n",
    "        valid_accuracy_str = lines0[-6]\n",
    "        train_loss_str     = lines0[-8]\n",
    "        train_accuracy_str = lines0[-9]\n",
    "        average_time_str   = lines0[-10]        \n",
    "        run_time_str       = lines0[-11]   \n",
    "        \n",
    "    else: \n",
    "        valid_loss_str     = lines0[-6]\n",
    "        valid_accuracy_str = lines0[-7]\n",
    "        train_loss_str     = lines0[-10]\n",
    "        train_accuracy_str = lines0[-11]\n",
    "        average_time_str   = lines0[-12]        \n",
    "        run_time_str       = lines0[-13]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time\n",
    "\n",
    "\"\"\" Extract number of total parameters for each net config from resman's diary file\"\"\"\n",
    "def parse_num_params(lines0):\n",
    "    line_str = ''.join(lines0)\n",
    "    idx = line_str.find(\"Total params\")\n",
    "    param_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(locale.atof(param_num))\n",
    "\n",
    "def extract_perf_dim(results_dir):\n",
    "    # Dim: subspace dim\n",
    "    # Acc: Accuracy\n",
    "    \n",
    "    # filename list of diary\n",
    "    diary_names = []\n",
    "    for subdir, dirs, files in os.walk(results_dir):\n",
    "        for f in files:\n",
    "            if f == 'diary':\n",
    "                fname = os.path.join(subdir, f)\n",
    "                diary_names.append(fname)\n",
    "\n",
    "    # print diary_names   \n",
    "\n",
    "    dim = []\n",
    "    for f in diary_names:\n",
    "        # print f\n",
    "        tmp_str = f.split('/')[-2]\n",
    "        if tmp_str.split('_')[-5]=='MLP':\n",
    "            d = int(tmp_str.split('_')[-4])\n",
    "            dim.append(d)\n",
    "\n",
    "    dim = list(set(dim))\n",
    "    dim = sorted(dim)  \n",
    "\n",
    "    print dim\n",
    "\n",
    "    diary_names_ordered = []\n",
    "    for d in dim:\n",
    "        for f in diary_names:\n",
    "            if '_MLP_'+str(d)+'_' in f and f.split('_')[-5]=='MLP':\n",
    "                # print \"%d is in\" % d + f\n",
    "                diary_names_ordered.append(f)           \n",
    "    # print diary_names_ordered\n",
    "    # intrinsic update method\n",
    "    Dim= []\n",
    "    Acc = []\n",
    "\n",
    "    for fname in diary_names_ordered:\n",
    "        tmp_str = fname.split('/')[-2]\n",
    "        d = int(tmp_str.split('_')[-4])\n",
    "        with open(fname,'r') as ff:\n",
    "            lines0 = ff.readlines()\n",
    "\n",
    "            try: \n",
    "                r = extract_num(lines0,False)[1]\n",
    "                # print \"%d dim:\\n\"% d + str(r) + \"\\n\"\n",
    "                Dim.append(d)\n",
    "                Acc.append(r)\n",
    "\n",
    "            except ValueError:\n",
    "                print \"%d dim:\\n\"%d + \"Error \\n\"\n",
    "                pass\n",
    "\n",
    "    return Dim, Acc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_perf_dim(Dim, Acc, Cx=None):\n",
    "    fig, ax = subplots(figsize=(6,5) )\n",
    "\n",
    "    font = {'size'   : 12}\n",
    "    matplotlib.rc('font', **font)\n",
    "\n",
    "    for i in range(len(Dim)):\n",
    "        if Cx == None:\n",
    "            if Acc[i]>Acc[0]*0.9 and i>10:\n",
    "                print \"d_{int}=\"+str(Dim[i]) + ', acc:' + str(Acc[i])\n",
    "                break\n",
    "        else: \n",
    "            if Acc[i]>Cx and i>10:\n",
    "                print \"d_{int}=\"+str(Dim[i]) + ', acc:' + str(Acc[i])\n",
    "                break\n",
    "                \n",
    "            \n",
    "    \n",
    "    plot(Dim[4:], Acc[4:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "    plot(Dim[i], Acc[i], 'o', mec='b', mfc='b', ms=10)\n",
    "    axhline(Acc[0], ls='-', color='k',label='baseline')\n",
    "    axhline(Acc[0] * .9, ls=':', color='k',label='solved')\n",
    "    plt.legend()\n",
    "    ax.set_xlabel('Subspace dimension $d$')\n",
    "    ax.set_ylabel('Validation accuracy')\n",
    "\n",
    "    # ax.set_title('Cifar: Untied_LeNet' )\n",
    "    plt.grid()\n",
    "    ax.set_ylim([0.0,1.01])\n",
    "    # fig.savefig(\"figs/fnn_mnistPL_W\"+str(width[i])+\"_L\"+str(depth[j])+\".pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1000, 2000, 3000, 4000, 5000, 5250, 5500, 5750, 6000, 6250, 6500, 6750, 7000, 7250, 7500, 7750, 8000, 8500, 9000, 10000, 12500, 15000, 20000, 30000, 40000, 50000, 60000, 70000, 79685]\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "1000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "2000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "3000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "4000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5000 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5250 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5500 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "5750 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6000 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6250 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6500 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "6750 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7000 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7250 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7500 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "7750 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8000 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "8500 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "9000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "10000 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "12500 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "15000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "30000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "40000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "50000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "60000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "70000 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "79685 dim:\n",
      "Error \n",
      "\n",
      "[0, 0, 0, 0, 0, 0] [0.5257, 0.5142, 0.5078, 0.5147, 0.5128, 0.5114]\n"
     ]
    }
   ],
   "source": [
    "results_root = '/home/users/chunyuan.li/public_results/chun/'\n",
    "\n",
    "\n",
    "exp_folder = 'results_cifar_MLP'\n",
    "results_dir = results_root + exp_folder\n",
    "Dim, Acc= extract_perf_dim(results_dir)\n",
    "\n",
    "print Dim, Acc\n",
    "# plot_perf_dim(Dim, Acc, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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